关键词: 联邦学习, 统计异质性, 客户端漂移, 分布式学习, 非独立同分布

Abstract: Federated learning is a distributed machine learning framework that emphasizes privacy protection, but faces significant challenges in addressing statistical heterogeneity. Statistical heterogeneity arises from the differences in data distribution across participating nodes, which may lead to model update biases, performance degradation of the global model, and instability in convergence. The main issues caused by statistical heterogeneity were analyzed in detail, including inconsistent feature distributions, imbalanced label distributions, asymmetrical data volumes, and varying data quality. A systematic review of existing solutions was provided, which include local correction, clustering methods, client selection optimization, aggregation strategy adjustments, data sharing, knowledge distillation, and decoupling optimization, with an evaluation of their advantages, disadvantages, and applicable scenarios. Furthermore, future research directions were discussed, such as device computing capacity awareness, model heterogeneity adaptation, privacy security mechanisms optimization, and enhancement of cross-task transferability, providing references for addressing statistical heterogeneity in practical applications.

Key words: federated learning, statistical heterogeneity, client drift, distributed learning, non-IID (non-Independent and Identically Distributed)