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• FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions FedGlu: Personalized federated-learning based glucose forecasting algorithm
  Abstract Background: Continuous glucose monitoring (CGM) devices allow real-time glucose readings leading to improved glycemic control. However, glucose predictions in the lower (hypoglycemia) and higher (hyperglycemia) extremes, referred as glycemic excursion…
  preprints Dave Darpit, Kathan Vyas, Jagadish Kumaran Jayagopal, Alfredo Garcia 等 2025 置信度 0.74
• Efficient Federated Learning Based On Domain Adaptation and Knowledge Distillation Losses
  Abstract Numerous devices nowadays generate vast amounts of data for learning. Traditional centralized learning necessitates transmitting all data to a central site, which conducts the model training. However, much of these data may be sensitive, leading custo…
  preprints Jui-Chieh Liu, Cooper Cheng-Yuan Ku, Shao-Ci Wang 2025 置信度 0.74
• Federated Learning for Distributed Multi-Robotic Arm Trajectory Optimization
  Manipulator path and trajectory planning is a significant aspect of robotics that involves defining an optimal trajectory for the manipulator to move from a starting position to a target position while avoiding obstacles and minimizing latency factors such as …
  preprints Fazal Khan, Zhou Meng 2025 置信度 0.74
• Optimization, Communication, and Personalization in Federated Learning for Massive Networks
  We consider the problem of collaborative model optimization over a distributed network of agents, each possessing locally held data drawn from potentially heterogeneous distributions. The system operates under constraints of limited communication, partial part…
  preprints 2025 置信度 0.74
• Distribution-Aware Federated Learning for Diabetes Prediction Using Tabular Clinical Data Under Non-IID and Class-Imbalanced Settings
  preprints 2026 置信度 0.74
• Privacy-Preserving and Communication-Efficient Federated Learning for Cloud-Scale Distributed Intelligence
  preprints 2025 置信度 0.74
• Privacy-Preserving and Communication-Efficient Federated Learning for Cloud-Scale Distributed Intelligence
  Abstract This study focuses on privacy protection and multi-party collaborative optimization in cloud computing environments. A federated learning framework is proposed, integrating differential privacy mechanisms and communication compression strategies. The …
  preprints Heyao Liu, Yue Kang, Yuchen Liu 2025 置信度 0.74
• Security and privacy concerns in Federated Learning systems: a systematic review
  Abstract Federated Learning is a Machine Learning solution that trains a global model by aggregating weights from different peers. Federated Learning does not require that data be shared among nodes; however, it is not exempt from privacy and/or security issue…
  preprints Rosa Di Salvo, Antonino Galletta 2025 置信度 0.74
• Federated Learning for Cybersecurity A Privacy-Preserving Approach
  preprints 2025 置信度 0.74
• Q-Attentive DT-QFL: Adaptive Time-Symmetry in Quantum Federated Learning using Quantum Self-Attention
  Abstract The unstoppable growth of the Industrial Internet of Things (IIoT) already has left the field of outright decentralization of data, provoking an urgent need in the creation of sensitive machine learning models that consider user privacy. The de facto …
  preprints Nahin Nasir, Aiman Hanif 2026 置信度 0.74
• Federated Learning
  crossref 2026-06-12T10:45:27Z 置信度 0.70
• Robust defense against inference attacks and differential privacy integration in federated learning
  crossref M.A.P. Chamikara, Mohan Baruwal Chhetri 2026-06-12T10:49:31Z 置信度 0.70
• Privacy-preserving federated learning: algorithms and guarantees
  crossref Xinwei Zhang, Xiangyi Chen, Bingqing Song, Prashant Khanduri 等 2024-02-23T09:48:33Z 置信度 0.70
• Table 1: Federated learning algorithms summary.
  crossref 2026-02-13T08:43:17Z 置信度 0.70
• Figure 7: Evaluation of federated learning.
  crossref 2025-05-08T04:35:49Z 置信度 0.70
• Adopting Federated Learning for Software-Defined Networks
  crossref Akarsh K. Nair, Jayakrushna Sahoo, Gaurav Jaswal 2024-05-30T10:30:21Z 置信度 0.70
• Federated Learning in the Age of Foundation Models
  crossref Ziyue Xu 2025-01-21T18:22:35Z 置信度 0.70
• Privacy-preserving federated learning in IoT for smart and sustainable healthcare
  crossref Shinu M. Rajagopal, Supriya M, Rajkumar Buyya 2026-06-12T10:49:31Z 置信度 0.70
• Optimizations for federated learning systems
  crossref Xinyu Zhou 2025-10-01T09:35:29Z 置信度 0.70
• Resilience of federated learning: perspectives on attacks and defenses
  crossref Pravija Raj P V, Ashish Gupta, Sajal K. Das 2026-06-12T10:49:31Z 置信度 0.70
• Federated Learning
  crossref 2023-01-24T19:05:52Z 置信度 0.70
• Revolutionizing Healthcare Systems with Federated Learning and Blockchain
  crossref Arati J. Vyavahare 2025-08-01T12:15:01Z 置信度 0.70
• Artificial Intelligence-Based Federated Learning in Healthcare
  crossref Priyadarshini Pattanaik, Nguyen Manh Cuong, Sanchit Sarhadi 2026-05-08T21:41:08Z 置信度 0.70
• Applications of Federated Learning in AI, IoT, Healthcare, Finance, Banking, and Cross-Domain Learning
  crossref Walaa Hassan, Habiba Mohamed 2024-11-29T17:15:57Z 置信度 0.70
• Application of Federated Learning in Telecommunications and Edge Computing
  crossref Utpal Mangla 2022-07-07T12:16:52Z 置信度 0.70
• Video for Dew Intelligence: Federated learning perspective
  crossref 2022-01-04T15:37:10Z 置信度 0.70
• Federated Machine Learning in Medical Science
  crossref Vivek Tomar, Swati Sharma, Sangeeta Arora, Aditya Singhal 2024-09-20T13:25:42Z 置信度 0.70
• Figure 2: Split federated learning with cGAN.
  crossref 2024-11-29T05:25:27Z 置信度 0.70
• SAFARI: Sparsity-Enabled Federated Learning with Limited and Unreliable Communications_supp1-3296624.pdf
  crossref Wenbo Ding 2023-07-19T17:14:32Z 置信度 0.70
• Introduction to Federated Learning Systems
  crossref Syed Zawad, Feng Yan, Ali Anwar 2022-07-07T12:16:52Z 置信度 0.70
• Private Parameter Aggregation for Federated Learning
  crossref K. R. Jayaram, Ashish Verma 2022-07-07T12:16:52Z 置信度 0.70
• Federated Learning
  crossref Jagjit Singh Dhatterwal, Kiran Malik, Kuldeep Singh Kaswan, Ahmed A. Elngar 2024-11-29T17:15:57Z 置信度 0.70
• Fusion Federated Learning
  crossref Mohanad Ali 2024-08-20T19:29:53Z 置信度 0.70
• Application of Artificial Intelligence and Federated Learning in Petroleum Processing
  The petroleum industry, characterized by its complex operations and substantial data generation, is on the cusp of a technological revolution with the integration of artificial intelligence (AI) and federated learning (FL). This chapter explores the applicatio…
  crossref Abdelaziz El-Hoshoudy 2024-11-29T17:15:57Z 置信度 0.70
• A Federated Learning based Alzheimer's Disease Prediction
  crossref S Suchitra, N Senthamarai, M Jeyaselvi, RJ Poovaraghan 2023-11-16T11:04:28Z 置信度 0.70
• Exploring Communication Efficient Strategies in Federated Learning Systems
  crossref Akarsh K. Nair, Jayakrushna Sahoo, Ebin Deni Raj 2024-05-30T10:30:21Z 置信度 0.70
• Federated Learning for Pervasive Systems
  crossref 2022-06-28T08:40:09Z 置信度 0.70
• Optimization strategies for federated learning
  crossref Tinghao Zhang 2025-02-05T01:55:31Z 置信度 0.70
• Navigating Privacy Concerns in Federated Learning A GDPR-Focused Analysis
  crossref G Anitha, A Jegatheesan 2023-11-16T06:04:28Z 置信度 0.70
• Figure 1: The citation relationship between “Federated Learning”, “Transfer Learning” and “Advertising”.
  crossref 2023-08-07T04:51:29Z 置信度 0.70
• Introduction to Federated Learning
  crossref Akash Sanghi, Rupanshi Agarwal, Gaurav Agarwal, Sachi Gupta 等 2024-09-20T17:25:42Z 置信度 0.70
• Breaking the boundaried and optimizing healthcare in the metaverse through federated learning
  crossref Naboshree Bhattacharya, Divya Bansal 2025-09-12T12:25:54Z 置信度 0.70
• Systems Bias in Federated Learning
  crossref Syed Zawad, Feng Yan, Ali Anwar 2022-07-07T12:16:52Z 置信度 0.70
• Client-Conditional Federated Learning via Local Training Data Statistics
  crossref Rickard Brännvall 2026-07-29T19:13:54Z 置信度 0.70
• Peer-to-peer federated learning
  crossref Alka Luqman 2026-08-06T04:42:20Z 置信度 0.70
• Table 5: Federated learning results (E1–E5).
  crossref 2026-06-30T08:06:20Z 置信度 0.70
• Figure 3: Federated learning with MNIST dataset.
  crossref 2026-02-04T08:57:09Z 置信度 0.70
• Copyright
  crossref 2024-02-23T09:48:19Z 置信度 0.70
• Secure Federated Learning
  crossref Bo Tang, Xingyu Li 2024-09-03T07:02:43Z 置信度 0.70
• Federated neuro-symbolic learning framework
  crossref Pengwei Xing 2025-11-03T09:21:27Z 置信度 0.70
• Journey toward Federated Learning
  crossref Mithra Venkatesan, Radhika Menon, Anju Kulkarni, S. Jayachitra 2025-06-20T18:53:47Z 置信度 0.70
• Federated Learning Approaches for Intrusion Detection Systems: An Overview
  crossref Akarsh K. Nair, Jayakrushna Sahoo, Gaurav Jaswal 2024-05-30T10:30:21Z 置信度 0.70
• A Critical Role for Federated Learning in IoT
  crossref Kaushal Kishor, Angeles Quezada, Satya Prakash Yadav 2024-09-20T17:25:42Z 置信度 0.70
• Federated Learning in Smart Cities
  crossref Suman Chahar, Kuldeep Kaswan 2025-10-03T21:19:49Z 置信度 0.70
• Data Leakage in Federated Learning
  crossref Xiao Jin, Pin-Yu Chen, Tianyi Chen 2022-07-07T12:16:52Z 置信度 0.70
• Figure 9: Dropout enabled randomization LSTM deep learning for federated learning process.
  crossref 2026-05-01T08:00:31Z 置信度 0.70
• The metaverse shift: adapting to decentralized computing in federated learning for healthcare
  crossref Manas Kumar Yogi, Mangadevi Atti 2025-09-12T12:25:54Z 置信度 0.70
• Introduction to federated learning, split learning and splitfed learning
  crossref Geetabai S. Hukkeri, Gururaj H.L., N.Z. Jhanjhi, Hong Lin 2024-10-04T08:10:23Z 置信度 0.70
• Auditable Session Admission for Cross-Silo Federated Learning
  crossref Enzo Fenoglio, Philip Treleaven 2026-07-29T19:10:37Z 置信度 0.70
• A Unifying Framework for Federated Learning
  crossref Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu, Yaoliang Yu 2022-09-30T18:05:01Z 置信度 0.70
• Secure Federated Learning
  crossref Yaochu Jin, Hangyu Zhu, Jinjin Xu, Yang Chen 2022-11-29T19:05:05Z 置信度 0.70
• A Study of Blockchain-Based Federated Learning
  crossref Samaneh Miri Rostami, Saeed Samet, Ziad Kobti 2022-09-30T18:05:01Z 置信度 0.70
• Naive Federated Learning Approaches
  crossref Dinesh C. Verma 2021-07-13T08:59:18Z 置信度 0.70
• FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneity
  crossref Özgü Göksu, Nicolas Pugeault 2026-01-20T20:38:34Z 置信度 0.70
• Demystifying Block-Cyclic Sampling for Federated Learning Using MNIST
  crossref Stefan Arnold, Dilara Fietta 2026-01-20T20:38:34Z 置信度 0.70
• Copyright
  crossref 2026-06-12T10:49:31Z 置信度 0.70
• Causal structure learning for adversarial robustness in federated learning
  crossref Karamveer Singh Sidhu 2026-07-16T18:36:13Z 置信度 0.70
• Quantization for Federated Learning
  crossref Mingzhe Chen, Shuguang Cui 2024-02-19T05:04:40Z 置信度 0.70
• Maximizing Fog Computing Efficiency with Federated Multi-Agent Deep Reinforcement Learning
  crossref G. Anitha, A. Jegatheesan 2023-11-16T11:04:28Z 置信度 0.70
• Federated Learning and Fairness
  crossref Annie Abay, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig 2022-07-07T12:16:52Z 置信度 0.70
• Patient-Driven Federated Learning (PD-FL)
  crossref A. Menaka Devi, V. Megala 2024-07-29T08:04:57Z 置信度 0.70
• The mechanism of federated learning
  crossref Dhanashri Vaishali, Harish Kumar, D. Poornima 2026-03-25T15:39:21Z 置信度 0.70
• Protected Shot-Based Federated Learning for Facial Expression Recognition
  crossref A. Sherly Alphonse Rao, J. V. Bibal Benifa 2024-05-30T10:30:21Z 置信度 0.70
• NIAA: Neuroplasticity-Inspired Adaptive Aggregation Method for Federated Learning
  crossref Vahideh Hayyolalam, Öznur Özkasap 2026-01-20T20:38:34Z 置信度 0.70
• Personalized Federated Learning
  crossref Kaushal Kishor 2022-02-02T12:03:00Z 置信度 0.70
• Federated Learning of Things - Expanding the Heterogeneity in Federated Learning
  The Internet of Things (IoT) has revolutionized how our devices are networked, connecting multiple aspects of our life from smart homes and wearables to smart cities and warehouses. IoT’s strength comes from the ever-expanding diverse heterogeneous sensors, ap…
  crossref Scott Kuzdeba 2024-05-21T18:51:09Z 置信度 0.70
• On the 5th Generation of Local Training Methods in Federated Learning
  crossref Peter Richtarik 2025-01-21T18:22:35Z 置信度 0.70
• Figure 5: Federated learning with fashion-MNIST dataset.
  crossref 2026-02-04T08:57:09Z 置信度 0.70
• Federated Learning and Its Classifications
  crossref M. Spoorthi, H. L. Gururaj 2024-06-07T01:34:57Z 置信度 0.70
• Federated learning and personalized medicine
  crossref Lingala Thirupathi, Gundekari Anusha, Thejoramnaresh Reddy Boya, Hari Chandra Prasad Cheerla 2025-03-20T10:28:47Z 置信度 0.70
• Federated Machine Learning in Medical Science
  crossref Vrinda Sachdeva, Gaurav Agarwal, Arun Kumar, Shailja Varshney 等 2024-09-20T17:25:42Z 置信度 0.70
• Medical data safety via federated machine learning
  crossref Anne Hartebrodt, Reza Nasirigerdeh, Jan Bamubach, David Benjamin Blumenthal 等 2022-08-15T14:55:26Z 置信度 0.70
• Data heterogeneity: a curse and a blessing for federated learning
  crossref Ziyue Xu, Shanshan Huang, Xiayu Xu, Xiaoxiao Li 2025-03-07T12:49:15Z 置信度 0.70
• Federated Learning with Edge Computing for Real‐Time Decision‐Making
  crossref A. Charles Mahimainathan 2026-06-23T12:51:06Z 置信度 0.70
• Diseases Detection System Using Federated Learning
  crossref P. Dhiman, S. Wadhwa, Amandeep Kaur 2024-07-29T08:04:57Z 置信度 0.70
• A collaborated federated learning for healthcare informatics: solution and challenges
  crossref Rahul Joshi, Suman Kumari, Krishna Pandey, Rhythma Badola 2025-09-12T12:25:54Z 置信度 0.70
• Intelligent Healthcare with Federated Learning: A Brief Investigation
  crossref Hengjie Ma 2026-02-19T17:33:18Z 置信度 0.70
• Federated learning framework with battery-aware clients
  crossref Andrea Augello, Priyesh Ranjan, Ashish Gupta, Federico Corò 等 2026-06-12T10:49:31Z 置信度 0.70
• Figure 3: Working principle of federated learning technique.
  crossref 2026-04-02T08:53:41Z 置信度 0.70
• Figure 1: IRS-enable federated learning aware system.
  crossref 2021-11-22T05:14:05Z 置信度 0.70
• Figure 6: ResNet18 architecture in split federated learning.
  crossref 2024-11-29T05:25:27Z 置信度 0.70
• Evolutionary Multi-objective Federated Learning
  crossref Yaochu Jin, Hangyu Zhu, Jinjin Xu, Yang Chen 2022-11-29T19:05:05Z 置信度 0.70
• Meta-Learning-Based Initialization-Free Aggregation in Federated Learning
  crossref Sergio Pérez-Picazo, Hiram Galeana-Zapién, Edwin Aldana-Bobadilla 2026-07-29T19:10:21Z 置信度 0.70
• Index
  crossref 2026-06-12T10:49:31Z 置信度 0.70
• Communication-Efficient Federated Learning
  crossref Kaushal Kishor 2022-02-02T12:03:00Z 置信度 0.70
• Carbon-Aware Federated Learning with Energy-Sensitive Scheduling for Sustainable AI
  crossref Jamsher Bhanbhro 2026-01-20T20:38:34Z 置信度 0.70
• Federated Learning and Privacy, Challenges, Threat and Attack Models, and Analysis
  crossref Sheema Madhusudhanan, Arun Cyril Jose, Reza Malekian 2024-05-30T10:30:21Z 置信度 0.70
• Table 9: Comparison between federated and centralized learning.
  crossref 2025-05-08T04:35:49Z 置信度 0.70
• Federated Learning for Medical Imaging
  crossref 2025-03-07T12:48:53Z 置信度 0.70
• Contents
  crossref 2026-06-12T10:49:31Z 置信度 0.70