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• Distributed Intelligence in the Computing Continuum
  crossref Schahram Dustdar 2025-01-21T18:22:35Z 置信度 0.70
• On the 5th Generation of Local Training Methods in Federated Learning
  crossref Peter Richtarik 2025-02-25T18:44:34Z 置信度 0.70
• Role of Federated Learning in Healthcare: A Review
  In the modern era, there is a boom in automating medical diagnosis by adopting emerging technologies and advanced applications of artificial intelligence. These technologies require a huge amount of data for training the models and precisely predicting the dis…
  crossref Geeta Rani, Meet Oza, Heta Patel, Vijaypal Singh Dhaka 等 2024-03-07T14:29:53Z 置信度 0.70
• SAFe‐Health: Guarding federated learning‐driven smart healthcare with federated defense averaging against data poisoning
  Abstract Federated learning (FL) serves as a decentralized training framework for machine learning (ML) models, preserving data privacy in critical domains such as smart healthcare. However, it has been found that attackers can exploit this decentralized learn…
  crossref Bhabesh Mali, Pranav Kumar Singh, Nabajyoti Mazumdar 2024-04-22T05:59:52Z 置信度 0.70
• I. Federated Learning: Pushing Forward Machine Learning Without Scarifising Privacy
  crossref Om Ghade, Supriya Narad 2025-02-21T18:37:11Z 置信度 0.70
• Graph Representation Federated Learning for Malware Detection in Internet of Health Things
  crossref Mohamed Amjath, Shagufta Henna 2024-07-08T14:38:22Z 置信度 0.70
• Federated Learning with MLPerfTiny Tasks and Server-side Momentum
  crossref Lawrence Roman A. Quizon, Anastacia B. Alvarez 2024-04-12T18:05:22Z 置信度 0.70
• RSF: Reinforcement learning based hybrid split and federated learning for edge computing environments
  crossref Alireza Soleimani, Negar Anabestani, Mahmoud Momtazpour 2024-09-19T17:22:07Z 置信度 0.70
• Federated learning-based machine learning for predicting brain tumor
  The swift advancements in artificial intelligence (AI) and machine learning have profoundly impacted the realm of medical research, particularly in the realm of diagnosing and treating intricate conditions such as brain tumors. These tumors, characterized by u…
  crossref Yuheng Ge 2024-07-18T06:30:17Z 置信度 0.70
• Optimizing Federated Learning Efficiency: Exploring Learning Rate Scheduling in Sparsified Ternary Compression
  crossref C Nithyaniranjana Murthy, Sh Manjula 2024-08-07T17:29:50Z 置信度 0.70
• Federated CINN Clustering for Accurate Clustered Federated Learning
  crossref Yuhao Zhou, Minjia Shi, Yuxin Tian, Yuanxi Li 等 2024-03-18T18:56:31Z 置信度 0.70
• Poisoning Attacks Against Non-IID Federated Learning with Mixed-Data Calibration
  crossref Xufei Zhang, Suleyman Uludag 2024-12-20T18:56:07Z 置信度 0.70
• Hybrid Machine Learning Based Integrated Network Scanning And Anomaly Framework
  Modern networks produced enormous volumes of data which made them increasingly vulnerable to advanced cyber threats. Traditional scanning tools and standalone anomaly detection systems fell short in identifying evolving or zero day attacks. This study proposed…
  datacite Sarthak Jain, Suyash, Upendra Kumar, Utsav Chauhan 等 2026 置信度 0.66
• Hybrid Machine Learning Based Integrated Network Scanning And Anomaly Framework
  Modern networks produced enormous volumes of data which made them increasingly vulnerable to advanced cyber threats. Traditional scanning tools and standalone anomaly detection systems fell short in identifying evolving or zero day attacks. This study proposed…
  datacite Sarthak Jain, Suyash, Upendra Kumar, Utsav Chauhan 等 2026 置信度 0.66
• PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction
  Reconstructing high-quality images from low-resolution inputs using Residual Dense Spatial Networks (RDSNs) is crucial yet challenging. It is even more challenging in centralized training where multiple collaborating parties are involved, as it poses significa…
  datacite He, Peilin, Joshi, James 2025 置信度 0.66 Machine Learning (cs.LG)Cryptography and Security (cs.CR)FOS: Computer and information sciences
• Deep Learning for Enhancing IoT Security and Trust
  The Internet of Things (IoT) has grown into a massive ecosystem connecting billions of heterogeneous devices, from household sensors to industrial machinery, and is expected to exceed 75 billion nodes by 2025. This rapid expansion has introduced critical chall…
  datacite Alireza Rahimi pour anaraki 2025 置信度 0.66 IoT SecurityTrust ManagementDeep LearningIntrusion DetectionLong Short-Term Memory
• EGBP Bioenergy Grid Physically-Calibrated Synthetic Dataset — 15 nodes, 365 days, hourly resolution
  131,400 node-hour observations across 15 bioenergy grid nodes (6 anaerobic digesters, 5 gasifiers, 4 CHP units) over 365 days at hourly resolution. Parameters calibrated to Faaij (2006) and Atashbar et al. (2016). Generated for: Ethical Guardrail Bandit Prunin…
  datacite Moroke, Ntebogang 2026 置信度 0.66
• Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets
  Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic environments and deployment in real multicenter healthcare settings. A prior architectural study by the same aut…
  datacite Tertulino, Rodrigo, Alencar, Laercio, Almeida, Ricardo 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Cryptography and Security (cs.CR)Computers and Society (cs.CY)FOS: Computer and information sciences
• In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and Analysis
  The rapid expansion of the Internet of Things (IoT) and its integration with backbone networks have heightened the risk of security breaches. Traditional centralized approaches to anomaly detection, which require transferring large volumes of data to central s…
  datacite Chaudhary, Devashish, Rajasegarar, Sutharshan, Pokhrel, Shiva Raj, Pan, Lei 等 2026 置信度 0.66 Machine Learning (cs.LG)Cryptography and Security (cs.CR)FOS: Computer and information sciences
• Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies
  Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automa…
  datacite Ahi, Kiarash, Valizadeh, Saeed 2026 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Computation and Language (cs.CL)FOS: Computer and information sciencesK.6.5; I.2.7
• Wireless Decentralized Federated Learning via Device Clustering and Inter-Cluster Link Enhancement
  Decentralized federated learning (DFL) dispenses with the central server of classical FL by utilizing peer-to-peer model exchanges among edge devices. This server-free architecture enables ad-hoc, flexible distributed learning in large device-to-device (D2D) n…
  datacite Zheng, William Weijia, Liu, Hang, Zhang, Ying-Jun Angela 2026 置信度 0.66 Information Theory (cs.IT)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciences
• MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning
  Previous studies on federated learning (FL) often encounter performance degradation due to data heterogeneity among different clients. In light of the recent advances in multimodal large language models (MLLMs), such as GPT-4v and LLaVA, which demonstrate thei…
  datacite Zhang, Jianyi, Yang, Hao Frank, Li, Ang, Guo, Xin 等 2024 置信度 0.66 Artificial Intelligence (cs.AI)Computation and Language (cs.CL)Machine Learning (cs.LG)FOS: Computer and information sciences
• UAVIDS-2025: A Benchmark Dataset for Intrusion Detection in UAV Networks Using Machine Learning Techniques
  UAVIDS-2025 is a comprehensive benchmark dataset designed for evaluating intrusion detection systems (IDS) in UAV (Unmanned Aerial Vehicle) swarm networks. The dataset was generated through extensive simulations using the NS-3.24 network simulator, with realis…
  datacite Zeng, Qingli, Bashir, Abdalrahman, Nait-Abdesselam, Farid 2025 置信度 0.66
• UAVIDS-2025: A Benchmark Dataset for Intrusion Detection in UAV Networks Using Machine Learning Techniques
  UAVIDS-2025 is a comprehensive benchmark dataset designed for evaluating intrusion detection systems (IDS) in UAV (Unmanned Aerial Vehicle) swarm networks. The dataset was generated through extensive simulations using the NS-3.24 network simulator, with realis…
  datacite Zeng, Qingli, Bashir, Abdalrahman, Nait-Abdesselam, Farid 2025 置信度 0.66
• Revolutionizing Drug Discovery and Pharmacovigilance Through Artificial Intelligence:A Comprehensive Systematic Review of Machine Learning Architectures, Real‎ ‎ World Evidence Integration, and Regulatory Compliance
  The integration of artificial intelligence (AI) and machine learning (ML) into pharmaceutical research has engendered a paradigm shift in how novel therapeutics are identified, optimized, and monitored. Conventional drug discovery pipelines spanning 10–15 year…
  datacite Darshan K R*1, Shubham Shivangekar2, Yash Vispute3, Gayatri Dhamane4, Parth Thorat5, Prathamesh Chavan6 2026 置信度 0.66 Artificial intelligence; drug discovery; pharmacovigilance; machine learning; deep learning; real‎ ‎ world evidence; natural language processing; graph neural networks; explainable AI; regulatory compliance; adverse drug events; generative models; federated learning; ADMET prediction.
• Revolutionizing Drug Discovery and Pharmacovigilance Through Artificial Intelligence:A Comprehensive Systematic Review of Machine Learning Architectures, Real‎ ‎ World Evidence Integration, and Regulatory Compliance
  The integration of artificial intelligence (AI) and machine learning (ML) into pharmaceutical research has engendered a paradigm shift in how novel therapeutics are identified, optimized, and monitored. Conventional drug discovery pipelines spanning 10–15 year…
  datacite Darshan K R*1, Shubham Shivangekar2, Yash Vispute3, Gayatri Dhamane4, Parth Thorat5, Prathamesh Chavan6 2026 置信度 0.66 Artificial intelligence; drug discovery; pharmacovigilance; machine learning; deep learning; real‎ ‎ world evidence; natural language processing; graph neural networks; explainable AI; regulatory compliance; adverse drug events; generative models; federated learning; ADMET prediction.
• D8.8: Report on European Interoperability Framework Contributions
  This deliverable (D8.8) presents the contributions of the PLIADES project to advancing the European Interoperability Framework (EIF) and interoperability standardization, with a focus on building a modular, scalable, and trustworthy data sharing ecosystem. The…
  datacite Hypertech (Greece) 2026 置信度 0.66 PLIADESDeliverableInnovationData SpacesAI
• D8.8: Report on European Interoperability Framework Contributions
  This deliverable (D8.8) presents the contributions of the PLIADES project to advancing the European Interoperability Framework (EIF) and interoperability standardization, with a focus on building a modular, scalable, and trustworthy data sharing ecosystem. The…
  datacite Hypertech (Greece) 2026 置信度 0.66 PLIADESDeliverableInnovationData SpacesAI
• EGBP Bioenergy Grid Physically-Calibrated Synthetic Dataset — 15 nodes, 365 days, hourly resolution
  131,400 node-hour observations across 15 bioenergy grid nodes (6 anaerobic digesters, 5 gasifiers, 4 CHP units) over 365 days at hourly resolution. Parameters calibrated to Faaij (2006) and Atashbar et al. (2016). Generated for: Ethical Guardrail Bandit Prunin…
  datacite Moroke, Ntebogang 2026 置信度 0.66
• A Coopetitive-Compatible Data Generation Framework for Cross-silo Federated Learning
  Cross-silo federated learning (CFL) enables organizations (e.g., hospitals or banks) to collaboratively train artificial intelligence (AI) models while preserving data privacy by keeping data local. While prior work has primarily addressed statistical heteroge…
  datacite Nguyen, Thanh Linh, Pham, Quoc-Viet 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Computational Engineering, Finance, and Science (cs.CE)Distributed, Parallel, and Cluster Computing (cs.DC)Computer Science and Game Theory (cs.GT)
• Communication-Aware Knowledge Distillation for Federated LLM Fine-Tuning over Wireless Networks
  Federated learning (FL) for large language models (LLMs) offers a privacy-preserving scheme, enabling clients to collaboratively fine-tune locally deployed LLMs or smaller language models (SLMs) without exchanging raw data. While parameter-sharing methods in t…
  datacite Zhang, Xinlu, Yan, Na, Su, Yang, Deng, Yansha 等 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Towards Transparent Mental Health Insights: An Explainable AI Model for Career-Related Depression and Anxiety Among University Students Using Structured Data
  Career anxiety and depression among university students present a growing challenge to mental health and academic achievement. This study proposes an Explainable AI (XAI) framework using multimodal data and Federated Learning (FL) to identify early indicators …
  datacite Azam, Arsham, Ali, Rasikh, Farhat, Tayyaba, Akram, Sheeraz 2026 置信度 0.66 Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Artificial Intelligence and Big Data Integration: A Systematic Literature Review of Technological Trends, Ethical Challenges, and Future Directions
  The convergence of Artificial Intelligence (AI) and Big Data is often celebrated as a transformative force, yet the academic literature on their integration remains scattered across technical, ethical, and sector-specific silos. This systematic review, guided …
  datacite Siregar, Torang 2026 置信度 0.66 Algorithmic bias, artificial intelligence, Big Data, edge computing, federated learning, generative AI, systematic literature review
• Artificial Intelligence and Big Data Integration: A Systematic Literature Review of Technological Trends, Ethical Challenges, and Future Directions
  The convergence of Artificial Intelligence (AI) and Big Data is often celebrated as a transformative force, yet the academic literature on their integration remains scattered across technical, ethical, and sector-specific silos. This systematic review, guided …
  datacite Siregar, Torang 2026 置信度 0.66 Algorithmic bias, artificial intelligence, Big Data, edge computing, federated learning, generative AI, systematic literature review
• Federated Learning for Feature Generalization with Convex Constraints
  Federated learning (FL) often struggles with generalization due to heterogeneous client data. Local models are prone to overfitting their local data distributions, and even transferable features can be distorted during aggregation. To address these challenges,…
  datacite Kim, Dongwon, Kim, Donghee, Shyn, Sung Kuk, Kim, Kwangsu 2026 置信度 0.66 Machine Learning (cs.LG)Machine Learning (stat.ML)FOS: Computer and information sciencesFOS: Computer and information sciences
• Role of AI in Healthcare Applications
  This review paper examines the role of Artificial Intelligence (AI) in modern healthcare applications. It discusses key AI technologies such as Machine Learning and Deep Learning and their use in disease detection, medical imaging, predictive healthcare, drug …
  datacite Tejas, Tejas Raj Pandey 2026 置信度 0.66
• Role of AI in Healthcare Applications
  This review paper examines the role of Artificial Intelligence (AI) in modern healthcare applications. It discusses key AI technologies such as Machine Learning and Deep Learning and their use in disease detection, medical imaging, predictive healthcare, drug …
  datacite Tejas, Tejas Raj Pandey 2026 置信度 0.66
• ELASTIC Newsletter #4: Project Highlights and Achievements (June 2025–May 2026)
  This issue of the ELASTIC Newsletter highlights the project's progress and achievements from June 2025 to May 2026. ELASTIC is advancing secure, efficient, and scalable orchestration for next-generation 6G networks by leveraging WebAssembly, eBPF, confidential…
  datacite Vasic, Jelena 2026 置信度 0.66 6GWasmWebAssemblyFaaSConfidential Computing
• ELASTIC Newsletter #4: Project Highlights and Achievements (June 2025–May 2026)
  This issue of the ELASTIC Newsletter highlights the project's progress and achievements from June 2025 to May 2026. ELASTIC is advancing secure, efficient, and scalable orchestration for next-generation 6G networks by leveraging WebAssembly, eBPF, confidential…
  datacite Vasic, Jelena 2026 置信度 0.66 6GWasmWebAssemblyFaaSConfidential Computing
• Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
  Class imbalance is a common problem in deep learning that severely degrades performance. In federated learning (FL), it is a critical factor contributing to non-identically distributed data (non-IID). Building on several previous attempts, we define and analyz…
  datacite Chung, Haengbok, Lee, Jae Sung 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data
  In Federated Learning, heterogeneity in client data distributions often means that a single global model does not have the best performance for individual clients. Consider for example training a next-word prediction model for keyboards: user-specific language…
  datacite Rokvic, Ljubomir, Danassis, Panayiotis, Faltings, Boi 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• FEDERATED LEARNING FOR PRIVACY-PRESERVING THREAT INTELLIGENCE SHARING IN DISTRIBUTED CYBERSECURITY ECOSYSTEMS
  Effective cybersecurity threat intelligence depends fundamentally on the breadth and timeliness of threat data — yet the organizations most capable of generating actionable intelligence are simultaneously most constrained in sharing it due to privacy regulatio…
  datacite Dr. Angira A., Patel, Nilam, Joshi, Vaidehi, Patel, Avani, Vagadiya 等 2026 置信度 0.66 Federated Learning; Privacy-Preserving Machine Learning; Threat Intelligence Sharing; Differential Privacy; Homomorphic Encryption; Cybersecurity Collaboration; Byzantine-Robust Aggregation; STIX/TAXII; Indicators of Compromise; Secure Multi-Party Computation; CrossOrganizational Security
• FEDERATED LEARNING FOR PRIVACY-PRESERVING THREAT INTELLIGENCE SHARING IN DISTRIBUTED CYBERSECURITY ECOSYSTEMS
  Effective cybersecurity threat intelligence depends fundamentally on the breadth and timeliness of threat data — yet the organizations most capable of generating actionable intelligence are simultaneously most constrained in sharing it due to privacy regulatio…
  datacite Dr. Angira A., Patel, Nilam, Joshi, Vaidehi, Patel, Avani, Vagadiya 等 2026 置信度 0.66 Federated Learning; Privacy-Preserving Machine Learning; Threat Intelligence Sharing; Differential Privacy; Homomorphic Encryption; Cybersecurity Collaboration; Byzantine-Robust Aggregation; STIX/TAXII; Indicators of Compromise; Secure Multi-Party Computation; CrossOrganizational Security
• Integration of AI in Cybersecurity: Current Trends with a Focused Look at Intrusion Detection Applications
  Artificial Intelligence (AI) is widely adopted today for its ability to detect patterns, automate tasks, and reduce time and cost across various applications. Its integration into Cybersecurity has garnered significant attention, particularly in areas such as …
  datacite Tazili, S., Mansour, A., Chkouri, M. Y. 2026 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)Networking and Internet Architecture (cs.NI)Signal Processing (eess.SP)
• EGBP Bioenergy Grid Physically-Calibrated Synthetic Dataset — 15 nodes, 365 days, hourly resolution
  131,400 node-hour observations across 15 bioenergy grid nodes (6 anaerobic digesters, 5 gasifiers, 4 CHP units) over 365 days at hourly resolution. Parameters calibrated to Faaij (2006) and Atashbar et al. (2016). Generated for: Ethical Guardrail Bandit Prunin…
  datacite Moroke, Ntebogang 2026 置信度 0.66
• BioHackathon Europe 2025 Report
  This report documents BioHackathon Europe 2025, ELIXIR's annual flagship collaborative hacking event, held 3 to 7 November 2025 at the Hotel Esplanade Resort & Spa, Bad Saarow, Germany. Organised by the ELIXIR Hub, the event brought together an international c…
  datacite van Wyk, Deborah, Anton, Mihail, Heil, Katharina F 2026 置信度 0.66 BioHackathon, ELIXIR, life sciences, bioinformatics, open science, FAIR data, research data management, interoperability, collaborative hacking, workflows, metadata, genomics, federated data, machine learning
• BioHackathon Europe 2025 Report
  This report documents BioHackathon Europe 2025, ELIXIR's annual flagship collaborative hacking event, held 3 to 7 November 2025 at the Hotel Esplanade Resort & Spa, Bad Saarow, Germany. Organised by the ELIXIR Hub, the event brought together an international c…
  datacite van Wyk, Deborah, Anton, Mihail, Heil, Katharina F 2026 置信度 0.66 BioHackathon, ELIXIR, life sciences, bioinformatics, open science, FAIR data, research data management, interoperability, collaborative hacking, workflows, metadata, genomics, federated data, machine learning
• BioHackathon Europe 2025 Report
  This report documents BioHackathon Europe 2025, ELIXIR's annual flagship collaborative hacking event, held 3 to 7 November 2025 at the Hotel Esplanade Resort & Spa, Bad Saarow, Germany. Organised by the ELIXIR Hub, the event brought together an international c…
  datacite van Wyk, Deborah, Anton, Mihail, Heil, Katharina F 2026 置信度 0.66 BioHackathon, ELIXIR, life sciences, bioinformatics, open science, FAIR data, research data management, interoperability, collaborative hacking, workflows, metadata, genomics, federated data, machine learning
• BioHackathon Europe 2025 Report
  This report documents BioHackathon Europe 2025, ELIXIR's annual flagship collaborative hacking event, held 3 to 7 November 2025 at the Hotel Esplanade Resort & Spa, Bad Saarow, Germany. Organised by the ELIXIR Hub, the event brought together an international c…
  datacite van Wyk, Deborah, Anton, Mihail, Heil, Katharina F 2026 置信度 0.66 BioHackathon, ELIXIR, life sciences, bioinformatics, open science, FAIR data, research data management, interoperability, collaborative hacking, workflows, metadata, genomics, federated data, machine learning
• Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity
  Federated reinforcement learning (FedRL) enables multiple agents to collaboratively train a global policy without sharing raw data, making it ideal for privacy-sensitive applications. However, FedRL faces challenges in heterogeneous environments where differin…
  datacite Pang, Yiran, Ni, Zhen, Zhong, Xiangnan 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Ransomware Attack Vectors, Detection Techniques, and Mitigation Strategies: A Comprehensive Survey
  Ransomware has turned to be one of the most severe and costly cybersecurity threats to organisations and individuals globally. This is a broad overview of ransomware looking at it in various dimensions: the way ransomware has evolved over the years since use a…
  datacite Manoj Mule, Nazma A. Inamdar 2026 置信度 0.66 RansomwareCybersecurityMachine LearningMalware DetectionAttack Vectors
• Ransomware Attack Vectors, Detection Techniques, and Mitigation Strategies: A Comprehensive Survey
  Ransomware has turned to be one of the most severe and costly cybersecurity threats to organisations and individuals globally. This is a broad overview of ransomware looking at it in various dimensions: the way ransomware has evolved over the years since use a…
  datacite Manoj Mule, Nazma A. Inamdar 2026 置信度 0.66 RansomwareCybersecurityMachine LearningMalware DetectionAttack Vectors
• The Use of Artificial Intelligence Techniques in Combating Cybercrime: A Descriptive Analytical Study
  Artificial Intelligence (AI) has emerged as a transformative technology in modern cybersecurity due to its ability to enhance threat detection, automate security processes, and improve decision-making capabilities. With the increasing complexity and frequency …
  datacite Adoum, Housni Moubarak Oumar 2026 置信度 0.66 Artificial Intelligence Cybercrime Cybersecurity Machine Learning Deep Learning Intrusion Detection Threat Detection
• The Use of Artificial Intelligence Techniques in Combating Cybercrime: A Descriptive Analytical Study
  Artificial Intelligence (AI) has emerged as a transformative technology in modern cybersecurity due to its ability to enhance threat detection, automate security processes, and improve decision-making capabilities. With the increasing complexity and frequency …
  datacite Adoum, Housni Moubarak Oumar 2026 置信度 0.66 Artificial Intelligence Cybercrime Cybersecurity Machine Learning Deep Learning Intrusion Detection Threat Detection
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
  Federated distillation has emerged as a promising collaborative machine learning approach, offering enhanced privacy protection and reduced communication compared to traditional federated learning by exchanging model outputs (soft logits) rather than full mode…
  datacite Mujtaba, Ahmed, Radchenko, Gleb, Prodan, Radu, Masana, Marc 2025 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• PRISMA‑Based Systematic Review of PINNs, TinyML, and Edge‑Cloud Frameworks for Real‑Time Photovoltaic Monitoring
  This project houses the protocol and supporting materials for a PRISMA 2020‑compliant systematic literature review (SLR) investigating the integration of Physics‑Informed Neural Networks (PINNs), Tiny Machine Learning (TinyML), and Edge‑Cloud Collaborative Arc…
  datacite Kawonga, Towani 2026 置信度 0.66 Computer EngineeringEngineering
• FEDERATED LEARNING IN LUNG CANCER RADIOMICS AND MEDICAL IMAGING: A META-SURVEY OF COLLABORATIVE MODELS
  Lung cancer is a major cause of cancer morbidity and death worldwide; new methods of testing are urgently needed but are not always available and accurate. Federated learning (FL) is an emerging paradigm in the machine learning field that shows significant pot…
  datacite SRIVIDYA.CH , DR. RAMA SUBRAMANIAN K , MADHUBALA.M 2026 置信度 0.66 Federated Learning, Lung Cancer Detection, Machine Learning, Privacy-Preserving, Decentralized Data, Deep Learning, Blockchain, Data Heterogeneity
• FEDERATED LEARNING IN LUNG CANCER RADIOMICS AND MEDICAL IMAGING: A META-SURVEY OF COLLABORATIVE MODELS
  Lung cancer is a major cause of cancer morbidity and death worldwide; new methods of testing are urgently needed but are not always available and accurate. Federated learning (FL) is an emerging paradigm in the machine learning field that shows significant pot…
  datacite SRIVIDYA.CH , DR. RAMA SUBRAMANIAN K , MADHUBALA.M 2026 置信度 0.66 Federated Learning, Lung Cancer Detection, Machine Learning, Privacy-Preserving, Decentralized Data, Deep Learning, Blockchain, Data Heterogeneity
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• 2025-CyberTraining PIMeeting-Poster-T3-CIDERS.pdf
  This poster introduces the T3-CIDERS project, a Train-the-Trainer initiative designed to build a national community of practice around cyberinfrastructure (CI)- and data-enabled cybersecurity research and education. The project equips faculty-student teams (Fu…
  datacite Jiang, Peng, Sosonkina, Masha, Wu, Hongyi, Purwanto, Wirawan 等 2025 置信度 0.66 Professional education and trainingHigh performance computingCybersecurity and privacy not elsewhere classified
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings
  ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kop…
  datacite Papageorgiou, Konstantinos 2026 置信度 0.66
• SoK: A Systematic Bidirectional Literature Review of AI & DLT Convergence
  The integration of Artificial Intelligence (AI) with Distributed Ledger Technology (DLT) has become a growing research area, yet contributions tend to cluster around specific application domains or examine only one direction of the integration, leaving the bro…
  datacite Kathia, Ali Irzam, Erinle, Yimika, Satybaldy, Abylay, Tasca, Paolo 等 2026 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• A Systematic Review Of Explainable Artificial Intelligence Techniques For Trustworthy Machine Learning Systems
  While machine learning models become increasingly predictive, their lack of transparency threatens trust in high-risk domains like healthcare, finance, and civil infrastructure. Explainable AI research, thus, mainly deals with the challenges associated with ma…
  datacite Dr M. Lavanya, Monisha B, Monika. G 2022 置信度 0.66
• A Systematic Review Of Explainable Artificial Intelligence Techniques For Trustworthy Machine Learning Systems
  While machine learning models become increasingly predictive, their lack of transparency threatens trust in high-risk domains like healthcare, finance, and civil infrastructure. Explainable AI research, thus, mainly deals with the challenges associated with ma…
  datacite Dr M. Lavanya, Monisha B, Monika. G 2022 置信度 0.66
• FGCS_2026_Paper_Data
  This project contains the data supporting the findings of the research paper entitled "MetaCS-FL: A Metaheuristic-Based Framework for Client Selection in Federated Learning Systems". @misc{nunes2025metacsfl, title = {{MetaCS-FL: A Metaheuristic-Based Framework…
  datacite Nunes, Alan Lira 2026 置信度 0.66
• Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation
  Federated Learning (FL) is increasingly being adopted in military collaborations to develop Large Language Models (LLMs) while preserving data sovereignty. However, prompt injection attacks-malicious manipulations of input prompts-pose new threats that may und…
  datacite Lee, Youngjoon, Park, Taehyun, Lee, Yunho, Gong, Jinu 等 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering
  Federated Learning (FL) enables collaborative model training across multiple clients without sharing private data. We consider FL scenarios wherein FL clients are subject to adversarial (Byzantine) attacks, while the FL server is trusted (honest) and has a tru…
  datacite Kritharakis, Emmanouil, Jakovetic, Dusan, Makris, Antonios, Tserpes, Konstantinos 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data
  Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data privacy. However, its performance is often hindered by redundant, malicious, or abnormal sam…
  datacite Ardıç, Emre, Genç, Yakup 2026 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• LongevityCommon
  Aging, codified in ICD-11 (code XT9T “Ageing-related,” 2018; code MG2A “Ageing-associated decline in intrinsic capacity,” 2025), requires integrative biomarker frameworks that extend beyond individual epigenetic clocks and wearable predictors. We present a hyp…
  datacite Tkemaladze, Jaba 2026 置信度 0.66 aging biomarkerZe complexity indexfederated learningdifferential privacycitizen science
• Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy
  Federated learning (FL) is a distributed machine learning method where multiple devices collaboratively train a model under the management of a central server without sharing underlying data. One of the key challenges of FL is the communication bottleneck caus…
  datacite Ardıç, Emre, Genç, Yakup 2026 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning
  Federated Learning (FL) typically assumes unconditional collaboration, a premise that overlooks the complexities of real-world, multi-stakeholder environments in which clients may need to exclude one another for strategic, regulatory, or competitive reasons. T…
  datacite Rosendal, Daan, Oprescu, Ana 2026 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• FedSWA: Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging
  For federated learning (FL) algorithms such as FedSAM, their generalization capability is crucial for real-word applications. In this paper, we revisit the generalization problem in FL and investigate the impact of data heterogeneity on FL generalization. We f…
  datacite junkang, Liu, Liu, Yuanyuan, Shang, Fanhua, Liu, Hongying 等 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciencesI.2.1
• SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening
  @article{rangwala2025sketchguard, title={SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening}, author={Rangwala, Murtaza and Azzedin, Farag and Sinnott, Richard O and Buyya, Rajkumar}, journal={arXiv preprint arXiv…
  datacite Murtaza Rangwala 2026 置信度 0.66
• Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge
  While Federated Learning (FL) mitigates direct data exposure, the resulting trained models remain susceptible to membership inference attacks (MIAs). This paper presents an empirical evaluation of Differential Privacy (DP) as a defense mechanism against MIAs i…
  datacite Bertoli, Gustavo de Carvalho 2026 置信度 0.66 Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Federated Clinical Learning Cooperative (FCLC)
  Background: Training clinical artificial intelligence (AI) models requires large, diverse datasets that are rarely available at a single institution due to privacy regulations (GDPR, HIPAA) and institutional risk aversion. Existing federated learning platforms…
  datacite Tkemaladze, Jaba 2026 置信度 0.66 federated learningdifferential privacyclinical AIdata governanceOMOP Common Data Model
• Federated Clinical Learning Cooperative (FCLC)
  Background: Training clinical artificial intelligence (AI) models requires large, diverse datasets that are rarely available at a single institution due to privacy regulations (GDPR, HIPAA) and institutional risk aversion. Existing federated learning platforms…
  datacite Tkemaladze, Jaba 2026 置信度 0.66 federated learningdifferential privacyclinical AIdata governanceOMOP Common Data Model
• Distributed Machine Learning Systems: Algorithms, Communication Eciency, and Convergence Guarantees
  The rapid growth of datasets and model sizes in modern machine learning hasmade distributed training not merely advantageous but essential. This survey provides a comprehensive review of distributed machine learning systems, with a focuson three interconnected…
  datacite Cherif, Ahmed 2026 置信度 0.66 distributed machine learning, federated learning, gradient compression, communication eciency, convergence theory, stochastic optimization, data parallelism
• Distributed Machine Learning Systems: Algorithms, Communication Eciency, and Convergence Guarantees
  The rapid growth of datasets and model sizes in modern machine learning hasmade distributed training not merely advantageous but essential. This survey provides a comprehensive review of distributed machine learning systems, with a focuson three interconnected…
  datacite Cherif, Ahmed 2026 置信度 0.66 distributed machine learning, federated learning, gradient compression, communication eciency, convergence theory, stochastic optimization, data parallelism
• AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models
  In this paper, we introduce analytic federated learning (AFL), a new training paradigm that brings analytical (i.e., closed-form) solutions to the federated learning (FL) with pre-trained models. Our AFL draws inspiration from analytic learning -- a gradient-f…
  datacite He, Run, Tong, Kai, Fang, Di, Sun, Han 等 2024 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Federated Learning for Privacy-Preserving Intrusion Detection in IoT-Enabled Smart Environments
  crossref Aditya Kapoor 2025-08-08T13:38:18Z 置信度 0.70
• BanditMatch: A Game-Theoretic Approach for Stable Preference Matching in Peer-to-Peer Federated Learning
  crossref Alka Luqman, Anupam Chattopadhyay, Zhang Ruichen, Dusit Niyato 2026-01-20T20:38:34Z 置信度 0.70
• Comparison of Federated Learning and Fair Federated Learning for Pneumonia Patient Classification
  Objectives: This study aims to compare the performance of federated learning (FL) and fair federated learning (FFL) in classifying pneumonia patients based on chest X-ray data. The primary focus is on assessing the accuracy and fairness of these models in hand…
  crossref Kyungmin Na, Dohyoung Kim, Youngho Lee 2025-03-11T00:45:04Z 置信度 0.70
• Review for "Breaking Interprovincial Data Silos: How Federated Learning Can Unlock Canada’s Public Health Potential"
  crossref 2026-03-14T21:10:36Z 置信度 0.70
• Fedecoselect: A Communication-Efficient Client Selection Strategy Via Predictive Similarity Estimation in Federated Learning
  crossref Aissa Hadj Mohamed, Daniel L. Guidoni, Luis F. G. Gonzalez, Leandro A. Villas 等 2026-01-20T20:38:34Z 置信度 0.70
• Federated Learning on Wheels: A Decentralized Approach to Privacy-Enhanced Data Collection in Internet of Vehicles
  Due to privacy issues and the scattered nature of data produced by vehicles, the Internet of Vehicles (IOV) poses considerable hurdles for data collecting. In this chapter, we examine the idea of “Federated Learning on Wheels” (FLoW), which provides a decentra…
  crossref Neha Sharma, Urvashi Sugandh, Jyoti Agarwal, Arvind Panwar 等 2025-04-25T10:34:14Z 置信度 0.70
• Adaptive Federated Learning for Personalised Affective Pain Management
  crossref Luca Bondin, Alexiei Dingli 2025-09-16T17:32:27Z 置信度 0.70