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• Decentralized Privacy-Preserving Federated Learning of Computer Vision Models on Edge Devices
  crossref Damian Harenčák, Lukáš Gajdošech, Martin Madaras 2026-03-19T14:04:01Z 置信度 0.70
• Communication-Efficient Federated Learning for Real-Time Anti-Money-Laundering Monitoring Authors
  To support real-time anti-money-laundering (AML) surveillance, this study introduces a communication-efficient federated learning (FL) protocol combining parameter sparsification, quantization, and adaptive client participation. The evaluation uses a dataset r…
  crossref Claire Fontaine, Mathis Laurent, Julien Moreau 2026-02-02T18:26:07Z 置信度 0.70
• Robust Split Federated Offline Reinforcement Learning under Heterogeneous Client Data
  This paper studies split federated offline reinforcement learning (SFORL) for learning from decentralized trajectory datasets without directly centralizing raw trajectories. Rather than relying on centralized trajectory collection or requiring each client to t…
  crossref Seungchan Yu, Jungchan Cho, Kyungjae Lee, Jungwoo Lee 2026-07-18T19:56:29Z 置信度 0.70
• LatentFed: Reliability-Aware Latent Representations for Clustered Federated Learning under Statistical Heterogeneity
  Federated learning suffers performance degradation under statistical heterogeneity, where clients follow different data distributions and local models drift toward incompatible optima. Clustered Federated Learning mitigates this issue by grouping clients with …
  crossref Vincenzo Altomare, Dipanwita Thakur, Antonella Guzzo 2026-05-28T12:41:11Z 置信度 0.70
• Advanced Topics: Transfer Learning and Federated Learning in PHM
  crossref Baris Aykent 2026-05-10T14:48:54Z 置信度 0.70
• IRW-FL: Improvement-Based Reliability Weighted Federated Learning
  crossref Zehua Li, Junnan Yang 2026-08-14T19:28:53Z 置信度 0.70
• Adaptive Federated Learning for energy-aware wireless edge networks in 6G environments
  crossref Mohammed Belghachi 2026-02-07T00:28:23Z 置信度 0.70
• Federated Learning and Collaborative AI in Medical Diagnostics
  Advancements in Federated Learning (FL) and collaborative Artificial Intelligence (AI) are reshaping medical diagnostics by enabling hospitals and research institutions to build powerful models without centralized data sharing, as patient information remains o…
  crossref Shaista Ashraf Farooqi 2026-06-11T20:45:37Z 置信度 0.70
• FedAR: Adaptive Client Selection Strategy for Heterogeneous Federated Learning
  crossref Vahideh Hayyolalam, Öznur Özkasap 2026-07-14T19:38:09Z 置信度 0.70
• MetaFed: A Novel Aggregation Strategy for Efficient Federated Learning
  crossref Vahideh Hayyolalam, Öznur Özkasap 2026-07-14T19:38:09Z 置信度 0.70
• Hardware-Aware Federated Learning for Speech Emotion Recognition
  crossref Beyazıt Bestami Yüksel, Emrah Dikbiyik 2026-08-11T19:15:01Z 置信度 0.70
• Fostering security in healthcare recommendation systems with quantum federated learning
  crossref Nithya Nedungadi, Sriram Sankaran, Amita Sharma 2026-05-29T12:06:47Z 置信度 0.70
• FedGC: Federated learning with geometry calibrated differential privacy
  crossref Jifei Hu, Hang Zhang 2026-07-20T08:48:04Z 置信度 0.70
• Federated learning: A stochastic approximation approach
  crossref Srihari P V, Anik Kumar Paul, Bharath Bhikkaji 2026-01-03T15:46:56Z 置信度 0.70
• FLEXible: Enabling Federated Learning in Android devices
  crossref Mario García-Márquez, Nuria Rodríguez-Barroso, M. Victoria Luzón, Francisco Herrera 2026-06-01T19:33:50Z 置信度 0.70
• Harnessing Entanglement in Quantum Federated Learning Under Efficiency Constraints
  crossref Shiva Raj Pokhrel 2026-05-07T19:51:14Z 置信度 0.70
• Generalizable Hyperparameter Optimization for Federated Learning on Non-IID Cancer Images
  crossref Elisa Ribeiro, Rodrigo Moreira, Larissa Moreira, André Backes 2026-03-19T05:16:19Z 置信度 0.70
• Federated Learning in the Presence of Non-IID Data: A Systematic Survey
  crossref Bijit Barman, Satyajit Sarmah 2026-08-17T19:14:22Z 置信度 0.70
• Federated Heterogeneous Graph Contrastive Learning for Privacy-preserving Recommendation
  crossref Yuan Wang, Yu Wang, Yiwen Zhang 2026-01-13T08:44:46Z 置信度 0.70
• Cross-Silo Federated Learning for Reliable Demand Forecasting and Inventory Optimization
  crossref Likhita Sreekakolapu, Lavanya Devi Golagani 2026-04-30T19:45:47Z 置信度 0.70
• On the Impact of Differential Privacy on Federated Neuromorphic Learning Accuracy
  crossref Luiz Pereira, Dalton Valadares, Mirko Perkusich, Kyller Gorgônio 2026-04-16T19:42:43Z 置信度 0.70
• Energy-Efficient Over-the-Air Federated Learning via Pinching Antenna Systems
  crossref Saba Asaad, Ali Bereyhi 2026-07-14T19:38:09Z 置信度 0.70
• Towards Secure and Robust Federated Learning
  crossref Islam Debicha, Tayeb Kenaza, Islam Hentous 2026-08-20T19:08:33Z 置信度 0.70
• Federated Learning: Unlocking Collaborative Machine Learning in Oman's Oil & Gas Sector Without Sharing Sensitive Data
  Abstract The oil and gas (O&G) industry in Oman is increasingly adopting data-driven and machine learning (ML) solutions to improve operational efficiency and decision-making. However, one of the main barriers to scaling ML across organizations is data-sha…
  crossref Sultan Alkaabi, Nasser Alhusaini, Nouf Al Noufli 2026-05-18T00:03:52Z 置信度 0.70
• Federated Learning–Driven Heart Disease Prediction using Machine and Deep Learning Models
  crossref Ranjitha H. M, Nagaraj Naik, Deepasree S Kumar 2026-07-01T19:35:13Z 置信度 0.70
• Smart grid false data injection detection through federated learning with deep learning models
  The security of smart grids is seriously threatened by false data injection (FDI) attacks. Falsified data is maliciously injected into the grid's measurement and control systems as part of these attacks, which might seriously disrupt the power supply and jeopa…
  crossref Raseel Alshamasi, Dina M. Ibrahim 2026-03-25T06:29:56Z 置信度 0.70
• Emerging Research Challenges, Legal Frameworks, and Real-World Insights in Secured Federated Learning Systems
  This chapter per the authors explores secured federated learning systems, focusing on privacy preservation, adversarial resilience, and regulatory compliance. It explains how federated learning enables collaborative model training without direct data sharing, …
  crossref Chirra Baburao, Nitu Tank, D. Rajyalakshmi, Aasha Soni 2026-02-20T15:26:17Z 置信度 0.70
• Entropy-guided meta-learning defense against data poisoning attacks in blockchain-assisted federated learning
  crossref Waheeb Algethami, Guowei Wu, Faisal Alshami, Muhanna Ali 2026-03-21T07:32:07Z 置信度 0.70
• Privacy-Preserving Federated Learning with Federated GANs for Early Heart Disease Prediction
  crossref Vivean Arya, Nami Desai, Vihaan Bhulla, Deepa Krishnan 2026-08-03T21:25:16Z 置信度 0.70
• Benchmarking the Robustness of Federated Learning Frameworks Under Adversarial Conditions
  Federated Learning (FL) has emerged as a promising instance that enables collaborative model training to be delivered while the privacy. However, its decentralized nature reveals FL systems at significant risks, including data and model poisoning, Byzantine fa…
  crossref Randhir Singh Baghel, Harish Reddy Gantla, Udit Mamodiya, Abrar Ahmed Syed 2026-02-20T15:26:17Z 置信度 0.70
• Federated Learning in Medical Diagnostics
  AI has demonstrated its necessity in healthcare through better disease forecasts. Conventional machine learning models function on data that are gathered in a centralized location, which require data exchange and thus cause worries about privacy of patients, d…
  crossref Uddalak Mitra, Shafiq Ul Rehman, Barnik Podder, Arpan Dutta 等 2026-02-20T15:26:17Z 置信度 0.70
• Federated learning-driven cloud intrusion detection using transfer learning and ablation frigate-fairy hybrid optimization
  crossref P. Senthil Raja, J. Sathiamoorthy 2026-07-19T07:21:12Z 置信度 0.70
• ORCHID-FL: On-Device Relationship-Aware Contextual Handling of Information via Decentralized Federated Learning
  crossref George Popescu-Craiova 2026-07-20T06:06:06Z 置信度 0.70
• Heterogeneous Adversarial Federated Learning
  crossref Wenrui Wang, Liping Yi, Gang Wang, Xiaoguang Liu 2026-04-21T21:25:57Z 置信度 0.70
• Don't Trust the Hidden Gradients!
  crossref Luke Sperling, Sandeep Kulkarni 2026-05-22T12:25:02Z 置信度 0.70
• Agent-Orchestrated Federated Representation Learning for Non-IID Data
  crossref Lei Yao, Tian Zhao 2026-08-20T19:08:51Z 置信度 0.70
• Federated Learning for Decentralized Smart Farm Network Applications
  crossref Mukesh Kumar Tripathi, Praveen Kumar Reddy, Vangara Nikitha, Nakshatra Reddy 等 2025-12-15T10:17:52Z 置信度 0.70
• Decentralized Tumor Classification with Federated Learning: A Privacy-Preserving Approach
  crossref Md. Nazmul Hossain Mir, Mir Nafiul Nagib, Afsana Alam Nova, Rahat Pervez 等 2025-10-31T19:42:51Z 置信度 0.70
• Packed and Unpacked Malware Detection by Means of Explainable Federated Machine Learning
  crossref Giovanni Ciaramella, Fabio Martinelli, Antonella Santone, Francesco Mercaldo 2026-07-20T07:16:21Z 置信度 0.70
• Privacy-Preserving Federated Learning for Multi-Hospital Patient Data Integration
  The exponential growth of medical data across hospitals presents significant opportunities for predictive healthcare analytics, yet stringent privacy laws and data fragmentation hinder collaborative research. This chapter introduces a Privacy-Preserving Federa…
  crossref Arul Selvam P., Tamije Selvy P. 2026-06-11T20:45:37Z 置信度 0.70
• Federated Split Learning: Distributed Local Training for Low Computational Unit Clients
  crossref Kazi Habibur Rahaman, Lilatul Ferdouse, Yang Liu 2026-05-06T19:38:02Z 置信度 0.70
• Federated Inter-Bank Default Prediction: Enabling Secure Credit Risk Assessment Without Data Sharing
  crossref Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Joaquin Del Rio, Oleksii Sliusarenko 等 2026-07-29T19:11:40Z 置信度 0.70
• Trust-aware Explainable Federated Learning (TEFL) for Privacy-Preserving Medical Diagnosis
  crossref Janice A. Abellana, Ephraimuel Jose L. Abellana 2026-05-06T19:38:02Z 置信度 0.70
• AWF-BI: An Adaptive Weighted Federated Learning Model for Privacy-Preserving Business Intelligence in Multi-Branch Retail Operations
  crossref Tarang Pande 2026-05-01T19:51:20Z 置信度 0.70
• A traffic-aware federated learning prediction framework with custom aggregation
  crossref Seerat Kaur, Sukhjit Singh Sehra, Darisuh Ebrahimi, Emad A. Mohammed 2025-12-29T07:25:17Z 置信度 0.70
• Regulatory challenges and compliance in federated learning (FL) for financial applications
  crossref K. Logeswaran, S. Savitha, P. Suresh, K. R. Prasanna Kumar 等 2026-03-25T15:39:21Z 置信度 0.70
• Research on Mechanical Fault Diagnosis Using Wavelet Empowered Federated Learning
  crossref 2026-08-12T06:57:34Z 置信度 0.70
• Federated learning with prototype-based adaptive domain adjustment
  crossref Leyuan Zhang, Fan Wan, Yang Long 2026-05-14T11:09:07Z 置信度 0.70
• Privacy Preserving Federated Learning Framework for Tuberculosis Management
  crossref Daphne Lopez, Brijendra Singh 2026-07-08T15:38:52Z 置信度 0.70
• Decentralized Federated Learning with Adaptive Model Compression for Precision Agriculture IoT Networks in Sub-Saharan Africa
  Precision agriculture holds tremendous potential for enhancing crop yields and food security across Sub-Saharan Africa, yet deploying machine learning systems in this region confronts fundamental barriers including limited network connectivity, constrained com…
  crossref Mohammed Ismail Behlim 2026-07-29T14:08:23Z 置信度 0.70
• EH-FLATO: Energy Harvesting-Aware Federated Learning and Adaptive Task Offloading for Resource-Constrained IoT Edge Networks
  The proliferation of energy-constrained Internet of Things (IoT) devices at the network edge demands intelligent strategies that jointly optimize computation offloading and collaborative model training without exhausting scarce energy budgets. Existing approac…
  crossref Dushyanta Dutta 2026-05-05T13:40:16Z 置信度 0.70
• The Convergence of Federated Learning for the Digital Healthcare Market: An Overview
  crossref Kanchan G. Rajput, Anil Sharma, Benila Susan Jacob, Pandya Abhishek Devendrabhai 2026-02-06T05:46:36Z 置信度 0.70
• Addressing Training Challenges in Federated Learning Using Adaptive Gradient Reshaping
  crossref Chenhua Yang 2026-03-10T19:50:38Z 置信度 0.70
• Privacy Protection Applications of Federated Learning in Financial Data Sharing
  crossref Li Cao 2026-06-02T12:09:11Z 置信度 0.70
• Federated Learning for Privacy-preserving Internet of Things (IoT) Security: A Decentralized Intrusion Detection Framework
  crossref Muhammad Shaharyar Ramzan 2026-08-25T08:11:37Z 置信度 0.70
• ReFLect: A Reputation-based Client Selection for Federated Learning in Mobile Networks
  crossref Subhash Sagar, Adnan Mahmood 2026-07-14T19:38:09Z 置信度 0.70
• Blockchain Integrated Federated Learning with Privacy-Preserving Neural Networks
  crossref Krishna Chaitanya Yarlagadda 2026-05-05T20:00:29Z 置信度 0.70
• Feature-driven layer specialization for label heterogeneous federated learning
  crossref Obed Jamir, Angshuman Paul 2026-01-05T07:34:41Z 置信度 0.70
• A Deep Learning-Driven Smart Agriculture Framework with Privacy-Preserving Federated Learning and Real-Time Monitoring
  crossref Malathi D, Selvameena P 2026-07-08T15:38:55Z 置信度 0.70
• A Dual-Learning Framework for Flash Flood Susceptibility: Benchmarking the Centralized-Federated Paradigm
  Flash floods are among the most destructive natural hazards, yet their accurate prediction remains challenging due to the complexity of hydro-geomorphological processes and, increasingly, data privacy constraints. This study presents a dual-learning framework …
  crossref Hafez Mirzapour, Ali Haghizadeh, Saeid Derikvand 2026-07-01T03:15:11Z 置信度 0.70
• FedTest: Enhancing Convergence and Mitigation of Adversarial Attacks in Federated Learning
  Federated learning (FL) has emerged as a leading paradigm for distributed machine learning modelsacross devices without sharing their local private data. Typical FL algorithms combine local modelweights in proportion to each device’s data size, but struggle wi…
  crossref Mustafa Ghaleb, Mohanad Obeed, Muhamad Felemban, Anas Chaaban 2026-07-09T06:36:52Z 置信度 0.70
• An Efficient Federated Learning Strategy for Scenarios with Restricted Computational Resources
  Federated Learning (FL) has emerged as an effective approach for training machine learning models across distributed devices while maintaining privacy and security. However, FL faces significant challenges related to algorithm performance, communication effici…
  crossref jun wen, hongqin huang, xiaoru zhang, xiaoli li 2026-05-28T14:50:21Z 置信度 0.70
• PFL-Log: A Personalized Federated Learning Approach for Log Anomaly Detection
  With the widespread adoption of distributed systems in cloud computing and the Internet of Things, log data has become a critical resource for system monitoring and anomaly detection, as it provides fine-grained insights into system behaviors and operational s…
  crossref Bingqing Luo, Jiacheng Gu, Jinya Su, Bin Xia 2026-03-24T04:37:51Z 置信度 0.70
• Breaking barriers in federated transfer learning: A systematic review on federated transfer learning for non-overlapping domains
  crossref Tasnim Binte Shiraj, Sobhana Jahan, Md. Rawnak Saif Adib, Md. Sazzadur Rahman 等 2026-02-06T16:30:28Z 置信度 0.70
• Communication-Efficient Federated Learning Framework for Autonomous Vehicles
  crossref Sima Sinaei, Mohammadreza Mohammadi, Mina Alibeigi 2026-03-27T19:47:50Z 置信度 0.70
• Cloud-Native Graph-Aware Federated Learning for Diabetic Retinopathy Diagnosis: Architecture, Orchestration and Graph Aggregation
  crossref Jesu Marcus Immanuvel Arockiasamy 2026-07-31T18:14:39Z 置信度 0.70
• AI-Integrated Federated Learning Architecture for Confidential Cyber security Event Prediction
  crossref Manjunadh Maddhuru 2026-07-29T19:10:43Z 置信度 0.70
• Integrating real-time data with predictive models for early disease detection in metaverse healthcare
  crossref Herat Joshi 2025-09-12T12:25:54Z 置信度 0.70
• Federated Learning-based Algorithm Design for Privacy Preservation in Cross-domain Data Sharing
  crossref Yuxin Wu 2026-04-22T02:38:10Z 置信度 0.70
• A Secure, SDP-based Federated Learning Architecture for Pervasive Smart-City Ecosystem
  The constant emergence of connected devices and the generation of large volumes of diverse data have significantly expanded both the scope and the attack surface of modern systems, particularly in pervasive smart-city environments. This growing surface present…
  crossref SAAD MAHMOUDI, TARIK EL MOUDDEN, Mohamed Amnai 2026-02-17T14:42:36Z 置信度 0.70
• INTRUSION DETECTION BASED ON FEDERATED LEARNING – A REVIEW
  datacite 1Abu Ubaida,2Khurram Zeeshan Haider*, 3Temur-ul-Hassan, 4Muhammad Azam Rasheed 2026 置信度 0.66
• INTRUSION DETECTION BASED ON FEDERATED LEARNING – A REVIEW
  datacite 1Abu Ubaida,2Khurram Zeeshan Haider*, 3Temur-ul-Hassan, 4Muhammad Azam Rasheed 2026 置信度 0.66
• IEEE Standard for Substrate-Native Integer Computing (Zone 0) — Measured Solution to AI Energy Crisis, Reproducibility, and Edge Learning
  Hello. Thank you for taking the time and interest in this work. During the current times, I know that it can be tougher and tougher to decide who is truthful with their work and who steals from others for their own vanity. I stand by my work. Over the course o…
  datacite Dragolich, Daniel 2026 置信度 0.66
• IEEE Standard for Substrate-Native Integer Computing (Zone 0) — Measured Solution to AI Energy Crisis, Reproducibility, and Edge Learning
  Hello. Thank you for taking the time and interest in this work. During the current times, I know that it can be tougher and tougher to decide who is truthful with their work and who steals from others for their own vanity. I stand by my work. Over the course o…
  datacite Dragolich, Daniel 2026 置信度 0.66
• Landslide Early Warning System using SAR Data and Machine Learning
  Abstract - Landslides continue to pose a significant threat to human lives, infrastructure, transportation networks, and ecological systems, particularly in mountainous and high-rainfall regions. Recent advances in artificial intelligence, remote sensing, and …
  datacite Pavithra R, Mrs. Divyashree G, Mrs. Amulya M P, Prajwal M 等 2026 置信度 0.66 Landslide Early Warning SystemMachine LearningRandom ForestXGBoostInSAR
• Landslide Early Warning System using SAR Data and Machine Learning
  Abstract - Landslides continue to pose a significant threat to human lives, infrastructure, transportation networks, and ecological systems, particularly in mountainous and high-rainfall regions. Recent advances in artificial intelligence, remote sensing, and …
  datacite Pavithra R, Mrs. Divyashree G, Mrs. Amulya M P, Prajwal M 等 2026 置信度 0.66 Landslide Early Warning SystemMachine LearningRandom ForestXGBoostInSAR
• Scaling Farm Autonomy Decentralized Control, Data, and Edge
  This review examines the landscape of decentralized agricultural automation systems, motivated by the need to synthesize findings from a rapidly evolving field. It addresses the shift from traditional centralized approaches towards more scalable, robust, and p…
  datacite Shaik, Mohammad Muheeth 2026 置信度 0.66 decentralized automationswarm roboticsfederated learningblockchainedge IoT
• Scaling Farm Autonomy Decentralized Control, Data, and Edge
  This review examines the landscape of decentralized agricultural automation systems, motivated by the need to synthesize findings from a rapidly evolving field. It addresses the shift from traditional centralized approaches towards more scalable, robust, and p…
  datacite Shaik, Mohammad Muheeth 2026 置信度 0.66 decentralized automationswarm roboticsfederated learningblockchainedge IoT
• Failure Propagation and Self-Correction in Multi-Agent LLM Systems: How Deliberative Consensus, Credit Assignment, and Architectural Isolation Jointly Determine Systemic Reliability
  Version 2 — revised in response to an external structural review and an automated critique pass. See "Response to Review" appendix in the PDF for the change log. Multi-agent LLM systems are increasingly deployed in settings where individual agent failures can …
  datacite Saluca Agentic AI Research Team 2026 置信度 0.66 AI-drafted synthesisarXivpreprint reviewv2
• TOWARDS ROBUST AND PRIVACY-PRESERVING ANTI-MONEY LAUNDERING SYSTEMS: A SYSTEMATIC REVIEW OF FEDERATED LEARNING AND GRAPH NEURAL NETWORKS FOR FINANCIAL CRIME DETECTION
  datacite Syed Muhammad Abbas,Dr. Jawaid Iqbal,Syed Hasnat Raza Zaidi 2026 置信度 0.66
• TOWARDS ROBUST AND PRIVACY-PRESERVING ANTI-MONEY LAUNDERING SYSTEMS: A SYSTEMATIC REVIEW OF FEDERATED LEARNING AND GRAPH NEURAL NETWORKS FOR FINANCIAL CRIME DETECTION
  datacite Syed Muhammad Abbas,Dr. Jawaid Iqbal,Syed Hasnat Raza Zaidi 2026 置信度 0.66
• TOWARDS ROBUST AND PRIVACY-PRESERVING ANTI-MONEY LAUNDERING SYSTEMS: A SYSTEMATIC REVIEW OF FEDERATED LEARNING AND GRAPH NEURAL NETWORKS FOR FINANCIAL CRIME DETECTION
  datacite Syed Muhammad Abbas,Dr. Jawaid Iqbal,Syed Hasnat Raza Zaidi 2026 置信度 0.66
• Distributed Deep Learning and Intelligent Soil–Water Analytics in Precision Agriculture: A Comprehensive Review
  Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies …
  datacite Lemenkova, Polina 2026 置信度 0.66 Land management and planningArtificial intelligenceMachine LearningDeep LearningDeep learning
• Distributed Deep Learning and Intelligent Soil–Water Analytics in Precision Agriculture: A Comprehensive Review
  Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies …
  datacite Lemenkova, Polina 2026 置信度 0.66 Land management and planningArtificial intelligenceMachine LearningDeep LearningDeep learning
• Artificial Intelligence-Driven Digital Phenotyping in Psychiatry: Clinical Utility, Ethical Challenges, and Future Directions
  Digital phenotyping, defined as the moment-by-moment quantification of individual-level human phenotype using data from personal digital devices, has emerged as a promising approach to psychiatric assessment and monitoring. When combined with artificial intell…
  datacite Anna Byr, Melania Majewska, Natalia Piasecka, Izabela Rafalska, Jakub Smagoń, Natalia Kornacka, Martyna Kaim, Joanna Bober, Magdalena Bochenek and Wiktoria Siewiera 2026 置信度 0.66
• Artificial Intelligence-Driven Digital Phenotyping in Psychiatry: Clinical Utility, Ethical Challenges, and Future Directions
  Digital phenotyping, defined as the moment-by-moment quantification of individual-level human phenotype using data from personal digital devices, has emerged as a promising approach to psychiatric assessment and monitoring. When combined with artificial intell…
  datacite Anna Byr, Melania Majewska, Natalia Piasecka, Izabela Rafalska, Jakub Smagoń, Natalia Kornacka, Martyna Kaim, Joanna Bober, Magdalena Bochenek and Wiktoria Siewiera 2026 置信度 0.66
• Federated Learning Generalization in Cross-Device IoT Malware Detection: A Comparative Study with Centralized Models
  This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How do federated learning models trained on the N-BaIoT dataset generalize to cross-device or cross-domain IoT malware detection, as evaluated by F1-score…
  datacite Assignee Research 2026 置信度 0.66 federatedlearningmodelstrainedN-BaIoT
• Federated Learning Generalization in Cross-Device IoT Malware Detection: A Comparative Study with Centralized Models
  This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How do federated learning models trained on the N-BaIoT dataset generalize to cross-device or cross-domain IoT malware detection, as evaluated by F1-score…
  datacite Assignee Research 2026 置信度 0.66 federatedlearningmodelstrainedN-BaIoT
• LifeMH-FL: A Lifecycle-Stratified Mental Health Corpus for Federated Learning
  LifeMH-FL is the first lifecycle-stratified mental health corpus designed for federated learning research. It contains 111,191 dual-labelled records partitioned across three women's health lifecycle cohorts: S1 — Menstrual (77,259 records): sourced from MENST …
  datacite Devadas, Sayooj 2026 置信度 0.66 federated learningmental healthwomen's healthlifecycleNLP
• LifeMH-FL: A Lifecycle-Stratified Mental Health Corpus for Federated Learning
  LifeMH-FL is the first lifecycle-stratified mental health corpus designed for federated learning research. It contains 111,191 dual-labelled records partitioned across three women's health lifecycle cohorts: S1 — Menstrual (77,259 records): sourced from MENST …
  datacite Devadas, Sayooj 2026 置信度 0.66 federated learningmental healthwomen's healthlifecycleNLP
• BIG DATA MANAGEMENT AND PROCESSING PARADIGMS: A COMPREHENSIVE SURVEY OF ARCHITECTURES, TECHNOLOGIES, AND FUTURE DIRECTIONS
  The exponential growth of data generated by modern digital systems has fundamentally transformed how organizations store, process, and extract value from information. Big Data — characterized by its volume, velocity, variety, veracity, and value (the 5Vs) — ha…
  datacite Ergashev Baxriddin Nomoz o'gli 2026 置信度 0.66
• BIG DATA MANAGEMENT AND PROCESSING PARADIGMS: A COMPREHENSIVE SURVEY OF ARCHITECTURES, TECHNOLOGIES, AND FUTURE DIRECTIONS
  The exponential growth of data generated by modern digital systems has fundamentally transformed how organizations store, process, and extract value from information. Big Data — characterized by its volume, velocity, variety, veracity, and value (the 5Vs) — ha…
  datacite Ergashev Baxriddin Nomoz o'gli 2026 置信度 0.66
• Optimized Human Disease Predication & Detection Using Artificial Intelligence
  The field of AI integration within healthcare, particularly for early disease detection, has become a cornerstone of research and development. This review delves into the transformative potential of AI in revolutionizing healthcare delivery. We explore its app…
  datacite Vaibhavi Kshatriya1*, Manoj Kumbhare2, Parag Kothawade3, Prashant Malpure4, Rupali Lambole5, Jyoti Datir6, Prachi Ahire7 2026 置信度 0.66 artificial intelligence, healthcare, disease prediction, disease detection, machine learning, deep learning, cancer detection, cardiovascular diseases, neurological disorders.
• Optimized Human Disease Predication & Detection Using Artificial Intelligence
  The field of AI integration within healthcare, particularly for early disease detection, has become a cornerstone of research and development. This review delves into the transformative potential of AI in revolutionizing healthcare delivery. We explore its app…
  datacite Vaibhavi Kshatriya1*, Manoj Kumbhare2, Parag Kothawade3, Prashant Malpure4, Rupali Lambole5, Jyoti Datir6, Prachi Ahire7 2026 置信度 0.66 artificial intelligence, healthcare, disease prediction, disease detection, machine learning, deep learning, cancer detection, cardiovascular diseases, neurological disorders.
• Federated Learning For Privacy-Preserving Security Systems
  The rapid escalation of cyber threats in decentralized environments has necessitated the development of collaborative defense mechanisms that do not compromise data sovereignty. Traditional centralized machine learning requires the aggregation of sensitive tel…
  datacite Vikram Iyer 2023 置信度 0.66
• Federated Learning For Privacy-Preserving Security Systems
  The rapid escalation of cyber threats in decentralized environments has necessitated the development of collaborative defense mechanisms that do not compromise data sovereignty. Traditional centralized machine learning requires the aggregation of sensitive tel…
  datacite Vikram Iyer 2023 置信度 0.66
• Machine Learning-Driven Anomaly Detection for Supply Chain Integrity in 5G Industrial Automation Systems
  The rapid deployment of 5G-enabled industrial automation systems has transformed global supply chains by enabling ultra-reliable low-latency communication (URLLC), real-time decision-making, and seamless integration of cyber-physical systems. However, the incr…
  datacite James, Ugoaghalam Uche 2022 置信度 0.66 Machine Learning; Anomaly Detection; Supply Chain Integrity; 5G Industrial Automation; Cyber-Physical Security
• Machine Learning-Driven Anomaly Detection for Supply Chain Integrity in 5G Industrial Automation Systems
  The rapid deployment of 5G-enabled industrial automation systems has transformed global supply chains by enabling ultra-reliable low-latency communication (URLLC), real-time decision-making, and seamless integration of cyber-physical systems. However, the incr…
  datacite James, Ugoaghalam Uche 2022 置信度 0.66 Machine Learning; Anomaly Detection; Supply Chain Integrity; 5G Industrial Automation; Cyber-Physical Security
• Artificial Intelligence for Automated Tumor Segmentation and Treatment Response Assessment
  Cancer remains one of the leading causes of mortality worldwide, necessitating continuous advancements in diagnostic and therapeutic strategies. Medical imaging plays a central role in oncology by facilitating tumor detection, delineation, staging, treatment p…
  datacite Meera Iyer*1, Karan Bhattacharya2, Shalini Nair3, Shatrughna Nagrik4, Vivek Rao5 2026 置信度 0.66 Artificial intelligence; Tumor segmentation; Treatment response assessment; Deep learning; Radiomics; Precision oncology; Medical imaging; Foundation models.
• Artificial Intelligence for Automated Tumor Segmentation and Treatment Response Assessment
  Cancer remains one of the leading causes of mortality worldwide, necessitating continuous advancements in diagnostic and therapeutic strategies. Medical imaging plays a central role in oncology by facilitating tumor detection, delineation, staging, treatment p…
  datacite Meera Iyer*1, Karan Bhattacharya2, Shalini Nair3, Shatrughna Nagrik4, Vivek Rao5 2026 置信度 0.66 Artificial intelligence; Tumor segmentation; Treatment response assessment; Deep learning; Radiomics; Precision oncology; Medical imaging; Foundation models.