Personalized Federated Learning with Low-Rank Pruning-Based Adaptation for Non-IID Data
Personalized Federated Learning (pFL) has emerged as a promising paradigm, while existing approaches face 3 limitations: granularity mismatch, resource waste, and conflict aggregation. This paper presents a Personalized Federated Learning with Low-rank Pruning-based Adaptation (pFedLoPA) framework. Instead of balancing global and local trade-offs, pFedLoPA decouples the model into client-specific cores and globally shared complements. It integrates low-rank adaptation to constrain optimization to a compact subspace, gradient-based pruning to identify personalized parameters, and complementary aggregation to exchange only relevant updates. This enables clients to retain critical knowledge locally while efficiently integrating global knowledge. Extensive experiments on CIFAR-10/100 with multiple network architectures demonstrate that pFedLoPA outperforms state-of-the-art methods in test accuracy (up to 94.31% on CIFAR-10) while reducing communication costs by over 70%.