A Review of Federated Learning Methods in Heterogeneous Scenarios

• Published in: IEEE transactions on consumer electronics 2024, Vol.70 (3), p.5983-5999
• Main Authors: Pei, Jiaming, Liu, Wenxuan, Li, Jinhai, Wang, Lukun, Liu, Chao
• Format: Article
• Language: English
• Subjects: Computational modeling; Consumer electronics; data heterogeneity; Data models; device heterogeneity; Electronics; Federated learning; Heterogeneity; Mathematical models; model heterogeneity; non-IID data distribution; Privacy; Surveys; Training; User experience
• ISSN: 0098-3063; 1558-4127
• DOI: 10.1109/TCE.2024.3385440

Federated learning emerges as a solution to the dilemma of data silos while safeguarding data privacy, particularly relevant in the consumer electronics sector where user data privacy is paramount. However, federated learning is generally employed in a heterogeneous scenario, consisting of various factors that influence the training efficiency and accuracy of the federated learning models. There are many classic references focusing on federated communications, federated robustness and federated fairness, conversely, few of them clarify and summary systematically the influence of heterogeneity on the effect of federated learning. Therefore, we provide an overview of three heterogeneous challenges faced by federated learning in practical applications: device heterogeneity, data heterogeneity and model heterogeneity, and analyze their influence on federated learning. This is especially crucial in consumer electronics, where heterogeneity directly influence the performance and user experience of AI-driven features. And then, we highlight current solutions, ideas and challenges to compare different strategies for facing heterogeneous problems and outline several directions of future work that are relevant to a wide range of research communities.