A Fair Federated Learning Framework With Reinforcement Learning
This addresses fairness and performance issues in federated learning for clients with heterogeneous data, but it is incremental as it builds on existing FL algorithms.
The authors tackled the problem of heterogeneous data distributions in federated learning, which causes slow convergence, performance degradation, and unfairness, by proposing PG-FFL, a reinforcement learning framework that assigns aggregation weights to clients, and it outperformed baseline methods in overall performance, fairness, and convergence speed.
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.