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

arXiv: https://arxiv.org/abs/1905.07210 · HTML: https://mlsanity.com/paper/1905.07210/

## Key facts

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

## Built on by (most cited first, 30 of 38)

- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations](https://mlsanity.com/paper/2103.10481/) (2021)
- [Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints](https://mlsanity.com/paper/2106.00490/) (2021)
- [FedGL: Federated Graph Learning Framework with Global Self-Supervision](https://mlsanity.com/paper/2105.03170/) (2021)
- [FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server](https://mlsanity.com/paper/2204.11536/) (2022)
- [Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT](https://mlsanity.com/paper/2202.01512/) (2022)
- [Federated Learning with GAN-based Data Synthesis for Non-IID Clients](https://mlsanity.com/paper/2206.05507/) (2022)
- [A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency](https://mlsanity.com/paper/2307.10655/) (2023)
- [DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing](https://mlsanity.com/paper/2210.02680/) (2022)
- [Fairness-Aware Client Selection for Federated Learning](https://mlsanity.com/paper/2307.10738/) (2023)
- [Feature Matching Data Synthesis for Non-IID Federated Learning](https://mlsanity.com/paper/2308.04761/) (2023)
- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations](https://mlsanity.com/paper/2405.09839/) (2024)
- [Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks](https://mlsanity.com/paper/2410.17431/) (2024)
- [Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training](https://mlsanity.com/paper/2201.12976/) (2022)
- [Federated Learning with Server Learning: Enhancing Performance for Non-IID Data](https://mlsanity.com/paper/2210.02614/) (2022)
- [Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data](https://mlsanity.com/paper/2408.05678/) (2024)
- [FedCC: Robust Federated Learning against Model Poisoning Attacks](https://mlsanity.com/paper/2212.01976/) (2022)
- [FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data](https://mlsanity.com/paper/2205.09305/) (2022)
- [DPP-based Client Selection for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2303.17358/) (2023)
- [On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks](https://mlsanity.com/paper/2404.18519/) (2024)
- [Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach](https://mlsanity.com/paper/2406.09776/) (2024)
- [Aggregation Delayed Federated Learning](https://mlsanity.com/paper/2108.07433/) (2021)
- [Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning](https://mlsanity.com/paper/2212.01109/) (2022)
- [Modeling Global Distribution for Federated Learning with Label Distribution Skew](https://mlsanity.com/paper/2212.08883/) (2022)
- [Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks](https://mlsanity.com/paper/2302.10747/) (2023)
- [PRIMA: Planner-Reasoner Inside a Multi-task Reasoning Agent](https://mlsanity.com/paper/2202.00531/) (2022)
- [Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Data](https://mlsanity.com/paper/2207.07493/) (2022)
- [Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data](https://mlsanity.com/paper/2211.13116/) (2022)
- [Price-Discrimination Game for Distributed Resource Management in Federated Learning](https://mlsanity.com/paper/2308.13838/) (2023)

## 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.