mlsanityData to 14 Oct 2026

Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data

arXiv 1905.07210 ↗First posted 17 May 2019cs.LG cs.DC stat.MLcited by 38

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Abstract (arXiv)

This paper proposes a cooperative mechanism for mitigating the performance degradation due to non-independent-and-identically-distributed (non-IID) data in collaborative machine learning (ML), namely federated learning (FL), which trains an ML model using the rich data and computational resources of mobile clients without gathering their data to central systems. The data of mobile clients is typically non-IID owing to diversity among mobile clients' interests and usage, and FL with non-IID data could degrade the model performance. Therefore, to mitigate the degradation induced by non-IID data, we assume that a limited number (e.g., less than 1%) of clients allow their data to be uploaded to a server, and we propose a hybrid learning mechanism referred to as Hybrid-FL, wherein the server updates the model using the data gathered from the clients and aggregates the model with the models trained by clients. The Hybrid-FL solves both client- and data-selection problems via heuristic algorithms, which try to select the optimal sets of clients who train models with their own data, clients who upload their data to the server, and data uploaded to the server. The algorithms increase the number of clients participating in FL and make more data gather in the server IID, thereby improving the prediction accuracy of the aggregated model. Evaluations, which consist of network simulations and ML experiments, demonstrate that the proposed scheme achieves a 13.5% higher classification accuracy than those of the previously proposed schemes for the non-IID case.

Key facts. Cited by 38 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 May 2019.

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Citing papers per year: 2021: 6; 2022: 14; 2023: 7; 2024: 9; 2025: 1; 2026: 1.

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  1. 2021Towards Fairness-Aware Federated Learningcited by 58
  2. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
  3. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 36
  4. 2021FedGL: Federated Graph Learning Framework with Global Self-Supervisioncited by 34
  5. 2022FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Servercited by 29
  6. 2022Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTcited by 25
  7. 2022Federated Learning with GAN-based Data Synthesis for Non-IID Clientscited by 25
  8. 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21
  9. 2022DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharingcited by 17
  10. 2023Fairness-Aware Client Selection for Federated Learningcited by 15
  11. 2023Feature Matching Data Synthesis for Non-IID Federated Learningcited by 13
  12. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
26 more
  1. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  2. 2024Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attackscited by 9
  3. 2022Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Trainingcited by 7
  4. 2022Federated Learning with Server Learning: Enhancing Performance for Non-IID Datacited by 6
  5. 2024Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Datacited by 6
  6. 2022FedCC: Robust Federated Learning against Model Poisoning Attackscited by 5
  7. 2022FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Datacited by 4
  8. 2023DPP-based Client Selection for Federated Learning with Non-IID Datacited by 3
  9. 2024On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networkscited by 3
  10. 2024Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approachcited by 3
  11. 2021Aggregation Delayed Federated Learningcited by 2
  12. 2022Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learningcited by 2
  13. 2022Modeling Global Distribution for Federated Learning with Label Distribution Skewcited by 2
  14. 2023Clustered Data Sharing for Non-IID Federated Learning over Wireless Networkscited by 2
  15. 2022PRIMA: Planner-Reasoner Inside a Multi-task Reasoning Agentcited by 1
  16. 2022Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Datacited by 1
  17. 2022Fed-TDA: Federated Tabular Data Augmentation on Non-IID Datacited by 1
  18. 2023Price-Discrimination Game for Distributed Resource Management in Federated Learningcited by 1
  19. 2025A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networkscited by 1
  20. 2021Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Samplingcited by 0
  21. 2023Coded Matrix Computations for D2D-enabled Linearized Federated Learningcited by 0
  22. 2024FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learningcited by 0
  23. 2024Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID Datacited by 0
  24. 2024Data Quality in Edge Machine Learning: A State-of-the-Art Surveycited by 0
  25. 2024BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networkscited by 0
  26. 2026SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learningcited by 0