关键词: 联邦学习, 公平, 个性化, 异构数据, 客户端选择

Abstract: As a distributed optimization paradigm, federated learning enables a large number of resource-constrained client nodes to collaboratively train models without sharing their data. However, traditional federated learning methods, such as fedAvg, often fail to adequately address fairness issues. In practical scenarios, data distributions are typically highly heterogeneous, and conventional aggregation operations may introduce biases towards certain clients, resulting in significant performance disparities across clients for the global model. To tackle this challenge, a novel algorithm named Federated Learning for Personalization and Fairness (fedPF) is proposed. fedPF aims to effectively mitigate inefficient aggregation behaviors in federated learning and, by exploring the correlations between the global model and local models, distributes personalized models among clients, ensuring a balanced performance distribution among clients while maintaining the performance of the global model. fedPF was evaluated and analyzed on Synthetic, MNIST, and CIFAR10 datasets, and its performance was compared with three other federated learning algorithms: fedprox, qfedavg, and fedavg. The experimental results demonstrate that fedPF achieves notable improvements in both effectiveness and fairness.

Key words: federated learning, fairness, personalization, heterogeneous data, client selection