Fairness-Aware Federated Learning Framework on Heterogeneous Data Distributions

Recent years have witnessed increasing privacy concerns towards machine learning. To protect privacy in machine learning, federated learning has been proposed as a decentralized privacy-preserving framework where clients upload the parameters rather than private data. However, training a fair federated learning model in heterogeneous environments is still challenging. First, heterogeneous data distributions lead the global model fail to show high accuracy on all distributions. Second, the federated learning training process exposes and exacerbates potential biases in heterogeneous training data. Third, the local bias of each client can be propagated through parameter sharing, biasing the global model. In this work, we propose a two-stage fairness-aware federated learning framework (HeteroFair) to achieve fairness under heterogeneous data distributions. Initially, we introduce the fairness constraint to the loss function and propose a local adaptive weighting algorithm to adjust the proportion of the fairness constraint, achieving fair training in heterogeneous environments. Then, we present a fairness-aware aggregation reweighting algorithm that reduces the mismatch between local and global fairness to achieve fair federated learning. Extensive evaluation results demonstrate the effectiveness of our proposed framework in achieving fairness and high accuracy under het-eroaeneous data distributions.