mlsanityData to 14 Oct 2026

A Fair Federated Learning Framework With Reinforcement Learning

arXiv 2205.13415 ↗First posted 26 May 2022cs.LGcited by 6

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Abstract (arXiv)

Federated learning (FL) is a paradigm where many clients collaboratively train a model under the coordination of a central server, while keeping the training data locally stored. However, heterogeneous data distributions over different clients remain a challenge to mainstream FL algorithms, which may cause slow convergence, overall performance degradation and unfairness of performance across clients. To address these problems, in this study we propose a reinforcement learning framework, called PG-FFL, which automatically learns a policy to assign aggregation weights to clients. Additionally, we propose to utilize Gini coefficient as the measure of fairness for FL. More importantly, we apply the Gini coefficient and validation accuracy of clients in each communication round to construct a reward function for the reinforcement learning. Our PG-FFL is also compatible to many existing FL algorithms. We conduct extensive experiments over diverse datasets to verify the effectiveness of our framework. The experimental results show that our framework can outperform baseline methods in terms of overall performance, fairness and convergence speed.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 23 of its 32 references that are in the map. First posted on arXiv 26 May 2022.

Builds on

23 of 32 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  7. 2018Federated Learning with Non-IID Datacited by 1,108
  8. 2020Adaptive Federated Optimizationcited by 1,046
  9. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  10. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  11. 2019Agnostic Federated Learningcited by 456
  12. 2019Fair Resource Allocation in Federated Learningcited by 394
11 more
  1. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 305
  2. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  3. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 175
  4. 2018Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goalscited by 103
  5. 2021Federated Learning with Fair Averagingcited by 61
  6. 2020Model-sharing Games: Analyzing Federated Learning Under Voluntary Participationcited by 60
  7. 2021Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learningcited by 50
  8. 2021One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learningcited by 39
  9. 2022Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoTcited by 15
  10. 2021H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learningcited by 14
  11. 2021Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learningcited by 7

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Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 1.

6 papers.

  1. 2022A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacycited by 7
  2. 2025Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacycited by 4
  3. 2023Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarizationcited by 1
  4. 2023FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learningcited by 1
  5. 2023Dynamic Fair Federated Learning Based on Reinforcement Learningcited by 1
  6. 2024A Framework for testing Federated Learning algorithms using an edge-like environmentcited by 0