Fairness and Effectiveness in Federated Learning on Non-independent and Identically Distributed Data

Abstract

Federated learning is a distributed machine learning method that protects privacy by allowing participants to train models locally rather than uploading data. However, federated learning has a significant barrier because of the non-independent and identically distributed (Non-IID) nature of each participant’s local data. FedFE, a novel fair and effective federated optimization algorithm, is presented in this paper. FedFE introduces momentum gradient descent in the federated training process and proposes a fair weighting strategy based on participant performance in training to eliminate the unfairness caused by the preference for some participants in the federated aggregation process. Experiments on a large number of Non-IID datasets have demonstrated that the proposed algorithm improves on existing baseline algorithms in terms of fairness, effectiveness, and convergence speed.