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• Federated Learning in Telecom Cloud Infrastructures: Enabling Privacy-Aware AI for Secure Data Collaboration
  crossref Rajesh Kasavalalji 2025-09-12T12:04:26Z 置信度 0.70
• Dha-Fl: Dynamic Hierarchical Attention Aggregation Framework for Heterogeneous Edge Federated Learning
  crossref weibai zhou, Li Rong 2025-08-04T13:35:27Z 置信度 0.70
• Cosifl: Collaborative Secure and Incentivized Federated Learning with Differential Privacy
  crossref Zhanhong Xie, Meifan Zhang, Lihua Yin 2025-08-25T20:41:44Z 置信度 0.70
• Federated Learning Based Skin Disease Prediction
  crossref P. Ananthi, Sukant A. K 2025-09-04T18:16:47Z 置信度 0.70
• Sign-Entropy Regularization for Personalized Federated Learning
  Personalized Federated Learning (PFL) seeks to train client-specific models across distributed data silos with heterogeneous distributions. We introduce Sign-Entropy Regularization (SER), a novel entropy-based regularization technique that penalizes excessive …
  crossref Koffka Khan 2025-06-04T11:14:12Z 置信度 0.70
• Federated Learning with Differential Privacy: Gaussian Mechanism or Laplacian Mechanism?
  crossref Wessam Mesbah 2025-06-26T17:40:25Z 置信度 0.70
• Weighted Over-the-Air Federated Learning
  crossref Seyed Mohammad Azimi-Abarghouyi, Leandros Tassiulas, Carlo Fischione 2025-09-03T17:49:08Z 置信度 0.70
• Federated Learning With Individualized Privacy Through Client Sampling
  crossref Lucas Lange, Ole Borchardt, Erhard Rahm 2025-10-13T17:39:05Z 置信度 0.70
• Enhancing Android Lock Pattern Security with Personalized Federated Learning and Multi-Modal Framework
  crossref Sina Apak, Peri Güneş 2025-10-13T17:39:05Z 置信度 0.70
• A Generative Adversarial based Approach for Continual Federated Learning with Non-IID Data
  crossref Akshat Sharma, JingTao Yao 2026-04-07T19:54:58Z 置信度 0.70
• Privacy-Preserving FL
  crossref Somanath Tripathy, Harsh Kasyap, Minghong Fang 2025-11-21T17:09:27Z 置信度 0.70
• Challenges and Opportunities in Federated Learning for the Internet of Everything (IoE)
  crossref Sumit Bansal, Piyush Kumar, Tanmay Raj, Saurabh Mishra 等 2025-09-26T05:43:14Z 置信度 0.70
• Integration of Federated Learning and Blockchain for Smart Cities
  crossref 2025-10-03T21:19:49Z 置信度 0.70
• Federated Learning for Agentic Gen AI in Financial Risk Management for National Financial Security
  crossref Satyadhar Joshi 2025-10-08T20:13:03Z 置信度 0.70
• Federated Learning Approaches for Privacy-Preserving Security Analytics in Distributed IoT Environments
  The proliferation of Internet of Things (IoT) devices across diverse and distributed environments has amplified the need for robust, scalable, and privacy-aware security analytics. Traditional centralized machine learning models struggle to address these conce…
  crossref Garba Sani 2025-05-07T21:24:06Z 置信度 0.70
• Byzantine-Robust Defenses
  crossref Somanath Tripathy, Harsh Kasyap, Minghong Fang 2025-11-21T17:09:27Z 置信度 0.70
• Fedfreeze: A Dual-Phase Layer Freezing Framework for Federated Learning
  crossref Di Wu, Leon Wong, Blesson Varghese 2025-05-28T06:16:18Z 置信度 0.70
• Federated Learning for BYOD-Enabled Mobile Mentorship: Ensuring Privacy in Cloud-Telecom Converged Networks
  crossref Thaker Bhavik 2025-12-09T21:15:11Z 置信度 0.70
• Privacy Computing in Cross-Device Federated Recommendation
  crossref Xiangjie Kong, Lingyun Wang, Mengmeng Wang, Guojiang Shen 2025-03-06T01:40:56Z 置信度 0.70
• List of Contributors
  crossref 2025-04-25T10:34:14Z 置信度 0.70
• Adaptive Local Training in Federated Learning
  crossref Donald Shenaj, Eugene Belilovsky, Pietro Zanuttigh 2025-12-05T18:36:04Z 置信度 0.70
• Adaptive Momentum Enhanced Second-Order Stochastic Federated Learning
  crossref Jiahao Zhang, Xiaokang Pan, Jingling Liu, Zhe Qu 2025-10-24T01:45:02Z 置信度 0.70
• Federated Learning For Privacy-Preserving AI
  .
  crossref Ansh Bansal 2025-11-04T08:24:41Z 置信度 0.70
• DDoS_FL: Federated Learning Architecture Approach against DDoS Attack
  crossref 2025-10-17T09:04:50Z 置信度 0.70
• Federated Learning Platform
  crossref M. A. Kolpaschikov, Ivan I. Kholod 2025-07-08T13:36:13Z 置信度 0.70
• FedFZY: A Novel Aggregation Method for Federated Learning
  crossref Koksal Erenturk 2025-07-10T17:45:06Z 置信度 0.70
• Topology-Driven Defense: Detecting Model Poisoning in Federated Learning with Persistence Diagrams
  crossref Narges Alipourjeddi, Ali Miri 2025-02-25T06:22:45Z 置信度 0.70
• A Federated Model with Self-Supervised Learning and Broad Learning for Distributed Epilepsy Subtyping
  crossref Zihan Dong, Sun Zhou 2026-03-17T20:18:48Z 置信度 0.70
• Federated Learning Setting for E-Learning Course Recommendations
  crossref Miloš Jolović, Dušan Kostić, Aleksandar Joksimović, Talib Tahirović 等 2025-06-13T16:22:04Z 置信度 0.70
• Edge Computing
  crossref Mei Kobayashi 2025-08-01T12:37:38Z 置信度 0.70
• Cooperative Detection Algorithm of Malicious Nodes Based on Federated Learning
  crossref 2025-08-19T11:31:11Z 置信度 0.70
• Privacy Preserving Tree: Zero Overhead Privacy Preservation in Cross-silo Decentralized Federated Learning
  Federated Learning (FL) in Edge Computing generally manifests in two forms: Cross-silo FL, for networks of Edge/Cloud servers, and Cross-device FL, for IoT devices connected to a single server. Cross-silo FL, benefiting from fewer participants, robust resource…
  crossref Tomsy Paul, Santhosh Kumar 2025-04-28T12:58:55Z 置信度 0.70
• Federated Learning Meets Blockchain: A Collaborative Framework for Mining Abac Policies
  crossref sara aboukadri, Aafaf Ouaddah, Abdellatif Mezrioui 2025-01-20T20:39:03Z 置信度 0.70
• Privacy in Federated Learning
  Federated learning (FL) represents a significant advancement in distributed machine learning, enabling multiple participants to collaboratively train models without sharing raw data. This decentralized approach enhances privacy by keeping data on local devices…
  crossref Jaydip Sen, Hetvi Waghela, Sneha Rakshit 2025-01-23T13:16:04Z 置信度 0.70
• Federated Graph Learning-Based Dynamic Modeling of Risk Correlation Networks Among Financial Institutions
  crossref Shijie Huang 2025-12-20T09:12:49Z 置信度 0.70
• Federated Learning with Differential Privacy: A Synergistic Approach to Private and Collaborative Machine Learning
  The data-driven machine learning (ML) has caused an implicit conflict between the usefulness of machine learning models and personal privacy. Although the centralized models of ML require enormous data in order to achieve the state-of-the-art performance, such…
  crossref Amira Fatima 2025-11-04T08:24:41Z 置信度 0.70
• Load-Aware Training Scheduling for Model Circulation-based Decentralized Federated Learning
  crossref Haruki Kainuma, Takayuki Nishio 2026-03-19T20:04:01Z 置信度 0.70
• Research on the Application of FedDyn Algorithm in Federated Learning Based on Taylor
  crossref Zijia Li 2025-09-30T10:26:59Z 置信度 0.70
• Prediction of EV charging load based on federated learning
  crossref Wanjun Yin, Jianbo Ji 2025-01-17T18:45:15Z 置信度 0.70
• Securing Industrial IoT with Cross-System Federated Learning for Malware Detection
  crossref Caihong Wang, Du Xu 2025-08-13T17:26:30Z 置信度 0.70
• Construction and Application of Federated Learning Model for Comprehensive Quality Assessment of Students
  crossref Peiwen Gao 2025-10-21T17:07:16Z 置信度 0.70
• Federated Learning for Secure and Resilient AI Systems
  Federated Learning is a transformative approach to building secure and resilient AI systems by enabling decentralized model training without exposing raw data. As part of Challenges and Solutions for Cybersecurity and Adversarial Machine Learning, this chapter…
  crossref Shaista Ashraf Farooqi 2025-06-06T09:33:01Z 置信度 0.70
• Machine Learning and Artificial Intelligence Fundamentals for Federated Systems
  crossref N. Vinaya Kumari, G. S. Pradeep Ghantasala, Pellakuri Vidyullatha, R. Rajesh Sharma 2025-05-27T16:38:31Z 置信度 0.70
• The Role of Federated Learning in AI-Powered Integrated Healthcare Solutions
  Federated Learning (FL) represents a revolutionary approach to artificial intelligence in the health care system, which addresses the important challenges of data lift, security and regulatory compliance, which enables severe ally insights. This paradigm allow…
  crossref Kahksha Ahmed, Ankit Baranwal, Neetu Sharma, Puneet Garg 等 2025-10-30T14:01:36Z 置信度 0.70
• Comment on “Secure and efficient multi-key aggregation for federated learning”
  crossref Guilin Guan, Yang Cao, Hongtao Xie 2025-12-18T02:38:21Z 置信度 0.70
• Privacy-Preserving Student Activity Classification in E-Learning using Hybrid Federated Learning
  crossref Vaishnavi N, Devi A 2026-01-14T20:37:30Z 置信度 0.70
• Coordinate-Wise Median in Byzantine Federated Learning
  crossref Melanie Cambus, Darya Melnyk, Tijana Milentijevic, Stefan Schmid 2025-07-15T07:07:04Z 置信度 0.70
• Federated Learning
  crossref Rituparna Saha, Amit Biswas 2025-03-23T13:09:28Z 置信度 0.70
• Byzantine-Resilient Federated Learning via Distributed Optimization
  crossref Yufei Xia, Wenrui Yu, Qiongxiu Li 2025-12-05T18:36:04Z 置信度 0.70
• Comparative Analysis of Machine Learning and Federated Learning in Healthcare
  crossref Stevan Stanković, Pavle Vuletić, Dražen Drašković 2025-08-19T18:07:00Z 置信度 0.70
• VCRFL: A Verifiable and Collusion-Resistant Privacy-Preserving Framework for Efficient Federated Learning
  crossref Dahe Huang, Yong Ding 2025-12-19T14:32:32Z 置信度 0.70
• Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control
  crossref Yongjie Fu, Lingyun Zhong, Zifan Li, Xuan Di 2025-04-05T11:45:32Z 置信度 0.70
• Leveraging Federated Learning for Smart Grids: A Comprehensive Survey
  crossref Muhammad Akbar Husnoo, Adnan Anwar, Md. Abdur Rahman 2025-09-25T14:38:24Z 置信度 0.70
• Federated Learning in the Diagnosis of Medical Diseases: A Survey
  crossref Shuoshi Zhu, Yisheng Ruan, Tianyuan Yu, Liang Bai 2025-10-13T17:39:05Z 置信度 0.70
• EPPFL: Entropy-based Personalized Privacy in Federated Learning
  crossref Yang Guo 2026-01-28T20:54:44Z 置信度 0.70
• Decision letter 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
• Efficient Personalized Federated Learning Based on Dynamic Knowledge Cache
  crossref Guan Wang, Jiaxin Liu 2025-08-26T15:36:52Z 置信度 0.70
• Federated Learning for Privacy-Preserving Data Analysis: A Conceptual Framework and Practical Considerations
  This article examines Edge Artificial Intelligence (Edge AI) as an emerging paradigm for real-time decision making in environments with limited computational and energy resources. The study highlights the limitations of cloud-centric AI architectures in latenc…
  crossref Azamat Nurgaziev 2025-12-15T18:11:21Z 置信度 0.70
• FLZT-IDS: Federated Learning with Zero-Trust Framework for Secure IoT Intrusion Detection
  crossref Muhammad Asad, Safa Otoum 2025-09-02T03:37:22Z 置信度 0.70
• Optimizing the MOON Algorithm Based on Cosine Annealing Learning Rate in Federated Learning
  crossref Sirui Meng 2026-01-29T21:18:38Z 置信度 0.70
• Multiparty Computation
  crossref Mei Kobayashi 2025-08-01T12:37:32Z 置信度 0.70
• Deep reinforcement learning based resource provisioning for federated edge learning
  crossref Xingyun Chen, Junjie Pang, Tonghui Sun 2024-08-30T14:56:15Z 置信度 0.70
• Metacampus as a Base for a Unite! Blended Intensive Programme (BIP): Competencies for Collaborative Teaching in Joint Programme (Report 04/2025)
  The report “Metacampus as a Base for a Unite! Blended Intensive Programme (BIP): Competencies for Collaborative Teaching in Joint Programmes” describes the Erasmus + Blended Intensive Program (BIP) held from June to October 2024 under the Unite! University Net…
  datacite Acosta-Garcia, Marcela, Galante, Lorenzo, Kauppinen, Tomi, Keller, Elizabeth 等 2025 置信度 0.66
• FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
  Federated learning (FL) is a collaborative machine learning approach that enables multiple clients to train models without sharing their private data. With the rise of deep learning, large-scale models have garnered significant attention due to their exception…
  datacite Zhan, Ziwei, Zhao, Wenkuan, Li, Yuanqing, Liu, Weijie 等 2024 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
• An insight on the timely diagnosis of diabetic retinopathy using traditional and ai-driven approaches
  Diabetic Retinopathy is a progressive microvascular complication of diabetes that requires early detection to improve patient outcomes. Traditional screening techniques, including fundus photography and optical coherence tomography, provide valuable diagnostic…
  datacite Asif, Malaika, ur Rehman, Fasih, Rashid, Zoya, Hussain, Altaf 等 2025 置信度 0.66 Diabetic Retinopathyophthalmologistsconvolutional neural networkretina fundus
• Federated Learning based on Self-Evolving Gaussian Clustering
  In this study, we present an Evolving Fuzzy System within the context of Federated Learning, which adapts dynamically with the addition of new clusters and therefore does not require the number of clusters to be selected apriori. Unlike traditional methods, Fe…
  datacite Ožbot, Miha, Škrjanc, Igor 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciencesI.2.6; I.5.368T05
• AI-Enhanced Triage and Clinical Decision Support Tools in Primary Care
  Background: Artificial intelligence (AI)-powered symptom checkers, risk stratification engines and embedded clinical decision support systems (CDSS) are now migrating from research centres to the front line of healthcare. By synthesising multimodal patient dat…
  datacite Arman, Ikbal Humay 2025 置信度 0.66 Artificial intelligenceelectronic health recordsfamily medicinesymptom checkerstriage
• AI-Enhanced Triage and Clinical Decision Support Tools in Primary Care
  Background: Artificial intelligence (AI)-powered symptom checkers, risk stratification engines and embedded clinical decision support systems (CDSS) are now migrating from research centres to the front line of healthcare. By synthesising multimodal patient dat…
  datacite Arman, Ikbal Humay 2025 置信度 0.66 Artificial intelligenceelectronic health recordsfamily medicinesymptom checkerstriage
• POPri: Private Federated Learning using Preference-Optimized Synthetic Data
  In practical settings, differentially private Federated learning (DP-FL) is the dominant method for training models from private, on-device client data. Recent work has suggested that DP-FL may be enhanced or outperformed by methods that use DP synthetic data …
  datacite Hou, Charlie, Wang, Mei-Yu, Zhu, Yige, Lazar, Daniel 等 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Cryptography and Security (cs.CR)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciences
• An Efficient and Adaptive Framework for Achieving Underwater High-performance Maintenance Networks
  With the development of space-air-ground-aqua integrated networks (SAGAIN), high-speed and reliable network services are accessible at any time and any location. However, the long propagation delay and limited network capacity of underwater communication netwo…
  datacite Gou, Yu, Zhang, Tong, Liu, Jun, Qi, Zhongyang 等 2025 置信度 0.66 Networking and Internet Architecture (cs.NI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Protocole "Long-Life Index" — Spécification ouverte de smart-contracts incitatifs pour la durabilité et la maintenance prédictive des biens physiques
  Abstract ENThis document, produced with the assistance of ChatGPT o3 and ChatGPT 5 Thinking, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable pate…
  datacite Pillet, Xavier 2025 置信度 0.66
• Protocole "Long-Life Index" — Spécification ouverte de smart-contracts incitatifs pour la durabilité et la maintenance prédictive des biens physiques
  Abstract ENThis document, produced with the assistance of ChatGPT o3 and ChatGPT 5 Thinking, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable pate…
  datacite Pillet, Xavier 2025 置信度 0.66
• Protocole "Long-Life Index" — Spécification ouverte de smart-contracts incitatifs pour la durabilité et la maintenance prédictive des biens physiques
  Abstract ENThis document, produced with the assistance of ChatGPT o3 and ChatGPT 5 Thinking, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable pate…
  datacite Pillet, Xavier 2025 置信度 0.66
• Harnessing Artificial Intelligence for the Future of Precision Medicine: Transforming Patient-Centric Care Through Data-Driven Innovation
  Abstract: Advancements in artificial intelligence (AI) are steadily reshaping the landscape of modern medicine, particularly in the context of precision healthcare. In 2024, the global AI healthcare market surged to $ 32.34 billion, with 80 % of hospitals adop…
  datacite Heli Patel1, Deep Sathwara1, Vivekraj Maheshwari2, and Disha Joshi1* 2025 置信度 0.66
• Harnessing Artificial Intelligence for the Future of Precision Medicine: Transforming Patient-Centric Care Through Data-Driven Innovation
  Abstract: Advancements in artificial intelligence (AI) are steadily reshaping the landscape of modern medicine, particularly in the context of precision healthcare. In 2024, the global AI healthcare market surged to $ 32.34 billion, with 80 % of hospitals adop…
  datacite Heli Patel1, Deep Sathwara1, Vivekraj Maheshwari2, and Disha Joshi1* 2025 置信度 0.66
• Generalizable Federated Learning using Client Adaptive Focal Modulation
  Federated learning (FL) has proven essential for privacy-preserving, collaborative training across distributed clients. Our prior work, TransFed, introduced a robust transformer-based FL framework that leverages a learn-to-adapt hypernetwork to generate person…
  datacite Ashraf, Tajamul, Gillani, Iqra Altaf 2025 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciences
• VEIL.AI's Next-Generation Anonymization enables cutting-edge research for children's diseases
  PHEMS was proud to contribute to the 5th European OHDSI Symposium, “Scaling up reliable evidence across Europe”, on June 3rd, 2024, aboard the historic Steam Ship Rotterdam in the Netherlands. The event brought together researchers and stakeholders from across…
  datacite Pentikäinen, Tuomo 2024 置信度 0.66 Data ScienceData AnonymizationPediatricsSecondary Data Analysis
• VEIL.AI's Next-Generation Anonymization enables cutting-edge research for children's diseases
  PHEMS was proud to contribute to the 5th European OHDSI Symposium, “Scaling up reliable evidence across Europe”, on June 3rd, 2024, aboard the historic Steam Ship Rotterdam in the Netherlands. The event brought together researchers and stakeholders from across…
  datacite Pentikäinen, Tuomo 2024 置信度 0.66 Data ScienceData AnonymizationPediatricsSecondary Data Analysis
• Harnessing Artificial Intelligence for the Future of Precision Medicine: Transforming Patient-Centric Care Through Data-Driven Innovation
  Abstract: Advancements in artificial intelligence (AI) are steadily reshaping the landscape of modern medicine, particularly in the context of precision healthcare. In 2024, the global AI healthcare market surged to $ 32.34 billion, with 80 % of hospitals adop…
  datacite Heli Patel, Deep Sathwara, Vivekraj Maheshwari, Disha Joshi 2025 置信度 0.66
• AI-Powered Visualization is Transforming Modern Healthcare
  Healthcare is being transformed by AI-driven visualization, which transforms complex data into useful insights. This paper synthesizes advancements in AI visualization tools—spanning medical imaging, electronic health records (EHR), genomics, and public health…
  datacite Rahman NaziL, Ashikur 2025 置信度 0.66 Community child healthHealth equityHealth promotionInjury preventionPreventative health care
• AI-Powered Visualization is Transforming Modern Healthcare
  Healthcare is being transformed by AI-driven visualization, which transforms complex data into useful insights. This paper synthesizes advancements in AI visualization tools—spanning medical imaging, electronic health records (EHR), genomics, and public health…
  datacite Rahman NaziL, Ashikur 2025 置信度 0.66 NaturopathyChiropracticTraditional Chinese medicine and treatmentsTraditional, complementary and integrative medicine not elsewhere classified
• Cooperative Perception: A Resource-Efficient Framework for Multi-Drone 3D Scene Reconstruction Using Federated Diffusion and NeRF
  The proposal introduces an innovative drone swarm perception system that aims to solve problems related to computational limitations and low-bandwidth communication, and real-time scene reconstruction. The framework enables efficient multi-agent 3D/4D scene sy…
  datacite Pourmandi, Massoud 2025 置信度 0.66 Artificial Intelligence (cs.AI)Robotics (cs.RO)FOS: Computer and information sciencesFOS: Computer and information sciencesI.2.6; I.2.9; I.2.10; I.4.8
• Uncovering Gradient Inversion Risks in Practical Language Model Training
  The gradient inversion attack has been demonstrated as a significant privacy threat to federated learning (FL), particularly in continuous domains such as vision models. In contrast, it is often considered less effective or highly dependent on impractical trai…
  datacite Feng, Xinguo, Ma, Zhongkui, Wang, Zihan, Chegne, Eu Joe 等 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Artificial Intelligence in Intrusion Detection Systems: Trends, Frameworks, and Future Directions for Cybersecurity
  In the last decade, intrusion detection systems (IDS) have grown out of signature‐based filters to complex, AI driven platforms that have the ability to identify novel and polymorphic threats in real time. This paper will look in detail at artificial intellige…
  datacite REDDY, YAKUB, Lingam, Dr. G. Shankar 2024 置信度 0.66
• Artificial Intelligence in Intrusion Detection Systems: Trends, Frameworks, and Future Directions for Cybersecurity
  In the last decade, intrusion detection systems (IDS) have grown out of signature‐based filters to complex, AI driven platforms that have the ability to identify novel and polymorphic threats in real time. This paper will look in detail at artificial intellige…
  datacite REDDY, YAKUB, Lingam, Dr. G. Shankar 2024 置信度 0.66
• CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics
  Recent research has highlighted the importance of data quality in scaling large language models (LLMs). However, automated data quality control faces unique challenges in collaborative settings where sharing is not allowed directly between data silos. To tackl…
  datacite Zhao, Wanru, Fan, Hongxiang, Hu, Shell Xu, Zhou, Wangchunshu 等 2025 置信度 0.66 Computation and Language (cs.CL)Multiagent Systems (cs.MA)FOS: Computer and information sciencesFOS: Computer and information sciences
• Unite! "Digital Campus" Community. How Cm.2 maintains and develops IT infrastructure for learning and teaching for the Unite! alliance. Status Quo 06/2025.
  Unite! Digital Campus aims to establish a seamless digital infrastructure for learning and teaching across the Unite! alliance. This description reflects the status as of June 2025. This poster presents the efforts of Cm.2 "Digital Campus" in maintaining and d…
  datacite Ebner, Martin, Schön, Sandra, Alcober, Jesus, Bertonasco, Roberto 等 2025 置信度 0.66
• Unite! "Digital Campus" Community. How Cm.2 maintains and develops IT infrastructure for learning and teaching for the Unite! alliance. Status Quo 06/2025.
  Unite! Digital Campus aims to establish a seamless digital infrastructure for learning and teaching across the Unite! alliance. This description reflects the status as of June 2025. This poster presents the efforts of Cm.2 "Digital Campus" in maintaining and d…
  datacite Ebner, Martin, Schön, Sandra, Alcober, Jesus, Bertonasco, Roberto 等 2025 置信度 0.66
• Comprehensive Survey of Outlier Detection in Wireless Sensor Networks
  The recent advancements in outlier detection techniques for Wireless Sensor Networks emphasize their significance in enhancing data integrity and system reliability. It categorizes detection methods into centralized, distributed, and hybrid architectures, with…
  datacite Padma Sree N, Dr. Malini M Patil 2025 置信度 0.66 Wireless Sensor Networks (WSNs), Outlier Detection, Anomaly Detection, Machine Learning, Deep Learning, Centralized Architecture, Distributed Detection, Real-Time Monitoring, Sensor Faults, Energy Efficiency, Dimensionality Reduction.
• Comprehensive Survey of Outlier Detection in Wireless Sensor Networks
  The recent advancements in outlier detection techniques for Wireless Sensor Networks emphasize their significance in enhancing data integrity and system reliability. It categorizes detection methods into centralized, distributed, and hybrid architectures, with…
  datacite Padma Sree N, Dr. Malini M Patil 2025 置信度 0.66 Wireless Sensor Networks (WSNs), Outlier Detection, Anomaly Detection, Machine Learning, Deep Learning, Centralized Architecture, Distributed Detection, Real-Time Monitoring, Sensor Faults, Energy Efficiency, Dimensionality Reduction.
• Data Harmonization, Standardization, and Collaboration for Diabetic Retinal Disease (DRD) Research: Report From the 2024 Mary Tyler Moore Vision Initiative Workshop on Data
  The 2024 Mary Tyler Moore Vision Initiative (MTM Vision) Workshop on Data convened to discuss best practices and specific considerations for building a comprehensive, shareable MTM Vision data lake. The workshop aimed to accelerate the development of new indic…
  datacite Domalpally, A, Fickweiler, W, Levine, , Goetz, KE 等 2024 置信度 0.66 HumansDiabetic RetinopathyInformation DisseminationBiomedical ResearchUnited States
• Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform
  Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly in terms of provisioning and orchestrati…
  datacite Anderlini, Lucio, Barbetti, Matteo, Bianchini, Giulio, Ciangottini, Diego 等 2025 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)Artificial Intelligence (cs.AI)Data Analysis, Statistics and Probability (physics.data-an)FOS: Computer and information sciencesFOS: Computer and information sciences
• Scalable Architectures for Distributed AI Training Models
  This paper focuses on the development and impact of scalable systems related to distributed training of artificial intelligence (AI) models. The rise in AI workloads stemming from deep learning models, such as generative transformers and vision-oriented archit…
  datacite Baskar, Sikkayan 2023 置信度 0.66 Scalable AIDistributed TrainingCloud ArchitecturesDeep Learning ModelsParallel Computing
• Scalable Architectures for Distributed AI Training Models
  This paper focuses on the development and impact of scalable systems related to distributed training of artificial intelligence (AI) models. The rise in AI workloads stemming from deep learning models, such as generative transformers and vision-oriented archit…
  datacite Baskar, Sikkayan 2023 置信度 0.66 Scalable AIDistributed TrainingCloud ArchitecturesDeep Learning ModelsParallel Computing
• ENHANCING FEDERATED LEARNING PERFORMANCE WITH KOLMOGOROV ARNOLD NETWORKS BASED ON DEEP LEARNING
  Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative machine learning,enabling model training across decentralized clients without sharing raw data [1]. Despite its promise, criticalchallenges persist in real-world FL deployme…
  datacite Athanas R. Sanga, XuanGou Wu 2025 置信度 0.66
• ENHANCING FEDERATED LEARNING PERFORMANCE WITH KOLMOGOROV ARNOLD NETWORKS BASED ON DEEP LEARNING
  Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative machine learning,enabling model training across decentralized clients without sharing raw data [1]. Despite its promise, criticalchallenges persist in real-world FL deployme…
  datacite Athanas R. Sanga, XuanGou Wu 2025 置信度 0.66
• Seven Samurai: Privacy Threat Types of LINDDUN
  This data set provides the mapping between the privacy threat types and the lower-level threat characteristics onto the privacy threats discussed in six distinct and domain-specific privacy threat taxonomy publications: 'A Survey on Privacy and Security of Int…
  datacite Van Landuyt, Dimitri 2025 置信度 0.66
• Seven Samurai: Privacy Threat Types of LINDDUN
  This data set provides the mapping between the privacy threat types and the lower-level threat characteristics onto the privacy threats discussed in six distinct and domain-specific privacy threat taxonomy publications: 'A Survey on Privacy and Security of Int…
  datacite Van Landuyt, Dimitri 2025 置信度 0.66
• Creating awareness for inclusion, diversity, equity and accessibility (IDEA): The 21 Day Challenge on Metacampus (Report 03/2025)
  Szczerba, Aaron & Edelsbrunner, Sarah (2025). Creating awareness for inclusion, diversity, equity and accessibility (IDEA): The 21 Day Challenge on Metacampus. In: “Unite! Digital Teaching and Learning - Success Story Report”, 3/2025, DOI: 10.3217/1rf11-5zj55 …
  datacite Szczerba, Aaron, Edelsbrunner, Sarah 2025 置信度 0.66
• Creating awareness for inclusion, diversity, equity and accessibility (IDEA): The 21 Day Challenge on Metacampus (Report 03/2025)
  Szczerba, Aaron & Edelsbrunner, Sarah (2025). Creating awareness for inclusion, diversity, equity and accessibility (IDEA): The 21 Day Challenge on Metacampus. In: “Unite! Digital Teaching and Learning - Success Story Report”, 3/2025, DOI: 10.3217/1rf11-5zj55 …
  datacite Szczerba, Aaron, Edelsbrunner, Sarah 2025 置信度 0.66