Year: 2025
Type: article
Abstract: In the Internet of Things (IoT), data distribution among diverse terminals exhibits substantial statistical heterogeneity. This imbalance can lead to skewness and accuracy degradation, ultimately affecting the generalization ability and robustness of Federated Learning (FL) models. Our work addresses these critical challenges by proposing a novel method, Meta-Equilibrized Federated Learning (MEFL), which integrates meta-learning with gradient-descent preservation and an equilibrated optimization aggregation mechanism based on gradient similarity and variance weighted adjustment. By alleviating the gradient biases caused by multi-step local updates from the source, MEFL effectively resolves the issues of inconsistency between global and local optimization objectives. MEFL optimizes trade-offs between local and global models, and provides an efficient solution for cross-domain data security deployment in IoT scenarios. Comprehensive experiments conducted on real-world datasets demonstrate that MEFL achieves at least 3.26% improvement in final test accuracy, and substantially lowers communication overhead, compared to the existing state-of-the-art baseline methods. The results demonstrate that MEFL exhibits superior performance and generalization capability in addressing personalization challenges with imbalanced non-IID data distributions.
Source: Entropy
DOI: 10.3390/e27060553
Authors: Jialu Tang, Yali Gao, Xiaoyong Li, Jia Jia
Institution: Beijing University of Posts and Telecommunications
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: gold
Estimated APC paid: $3,137
Awards: G3035818605, G8018519254
OpenAlex ID: W4410721900