mlsanityData to 7 Oct 2026

FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring

arXiv 2012.07450 ↗First posted 14 Dec 2020cs.NI cs.AI cs.DCcited by 61

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Abstract (arXiv)

In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and recent development in machine learning, smart healthcare has seen many successful stories. However, existing approaches for in-home health monitoring do not pay sufficient attention to user data privacy and thus are far from being ready for large-scale practical deployment. In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global model in the cloud from multiple homes at the network edges and achieves data privacy protection by keeping user data locally. To cope with the imbalanced and non-IID distribution inherent in user's monitoring data, we design a generative convolutional autoencoder (GCAE), which aims to achieve accurate and personalized health monitoring by refining the model with a generated class-balanced dataset from user's personal data. Besides, GCAE is lightweight to transfer between the cloud and edges, which is useful to reduce the communication cost of federated learning in FedHome. Extensive experiments based on realistic human activity recognition data traces corroborate that FedHome significantly outperforms existing widely-adopted methods.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Dec 2020.

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Citing papers per year: 2021: 7; 2022: 12; 2023: 17; 2024: 17; 2025: 7; 2026: 1.

61 papers.

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  1. 2021Towards Personalized Federated Learningcited by 462
  2. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  3. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  4. 2022FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clientscited by 46
  5. 2021Federated Learning Meets Natural Language Processing: A Surveycited by 42
  6. 2022A Survey on Heterogeneous Federated Learningcited by 42
  7. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  8. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  9. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  10. 2022Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learningcited by 28
  11. 2023Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networkscited by 19
  12. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
38 more
  1. 2024An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learningcited by 17
  2. 2022Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verificationcited by 14
  3. 2023Remote patient monitoring using artificial intelligence: Current state, applications, and challengescited by 14
  4. 2023FedBone: Towards Large-Scale Federated Multi-Task Learningcited by 14
  5. 2024TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networkscited by 12
  6. 2024Green Federated Learning: A new era of Green Aware AIcited by 12
  7. 2023Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Applicationcited by 11
  8. 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
  9. 2024Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selectioncited by 10
  10. 2021Personalized Semi-Supervised Federated Learning for Human Activity Recognitioncited by 9
  11. 2024Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Surveycited by 9
  12. 2022Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspectivecited by 8
  13. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  14. 2023Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Datacited by 7
  15. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  16. 2022On the Design of Communication-Efficient Federated Learning for Health Monitoringcited by 6
  17. 2024MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoTcited by 6
  18. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  19. 2023FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learningcited by 5
  20. 2021Federated Learning: Issues in Medical Applicationcited by 4
  21. 2022Personalized Federated Learning with Exact Stochastic Gradient Descentcited by 4
  22. 2024FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Qualitycited by 4
  23. 2021Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Surveycited by 3
  24. 2023Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learningcited by 3
  25. 2023PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learningcited by 3
  26. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  27. 2024Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systemscited by 3
  28. 2024Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analyticscited by 3
  29. 2022Heterogeneous Federated Learning on a Graphcited by 2
  30. 2023FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learningcited by 2
  31. 2023Federated learning-outcome prediction with multi-layer privacy protectioncited by 2
  32. 2025pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixupcited by 2
  33. 2025Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneitycited by 2
  34. 2023FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learningcited by 1
  35. 2024SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farmscited by 1
  36. 2024Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learningcited by 1
  37. 2024FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regionscited by 1
  38. 2025Personalized Recommendation Models in Federated Settings: A Surveycited by 1

50 most cited of 61 All 61 (JSON)