Personalized client-edge-cloud hierarchical federated learning in mobile edge computing

作者：Chunmei Ma, Xiangqian Li, Baogui Huang, Guangshun Li, Fengyin Li · 发表于：Journal of Cloud Computing Advances Systems and Applications · 年份：2024 · DOI：10.1186/s13677-024-00721-w · 被引用次数：10 · 研究领域：Privacy-Preserving Technologies in Data、Recommender Systems and Techniques、Cryptography and Data Security

Mobile edge computing aims to deploy mobile applications at the edge of wireless networks. Federated learning in mobile edge computing is a forward-looking distributed framework for deploying deep learning algorithms in many application scenarios. One challenge of federated learning in mobile edge computing is data heterogeneity since the unified model of federated learning performs poorly when client data are non-independent and identically distributed. Personalized federated learning can obtain amazing models in scenarios where client data are non-independent and identically distributed. This is because the personalized model captures the features of users’ local data more accurately than the unified model. The personalized federated learning problem under two-tier (server-client) federated learning structures has been widely studied and applied. However, a lot of research results exhibit three distinct limitations: 1) suboptimal communication efficiency, 2) slow model convergence, and 3) underutilization of the relationships within user data, resulting in lower accuracy of personalized models. In this paper, we present the first personalized federated learning algorithm based on the client-edge-cloud structure. The edge server is responsible for model personalization and employs a learnable mixing parameter to mix the local model and the global model. We also utilize two learnable normalization parameters trained by clients to improve the performance of personalized models...