PFL-AP: Personalized Federated Learning With Adaptive Affine Calibration and Prototype-Driven Knowledge Fusion for Non-IID Data

Abstract

Personalized federated learning (PFL) based on model decoupling has emerged as a prominent paradigm for mitigating statistical heterogeneity. However, most existing methods rely on static or oversimplified local representations, neglecting the severe feature misalignment during global aggregation. This inevitably precipitates representation degradation and convergence difficulties, especially under highly heterogeneous data distributions. To this end, we propose a novel framework termed PFL-AP for adaptive client representation calibration and knowledge fusion. Specifically, we design an adaptive affine calibration (AAC) mechanism that empowers each client with affine parameters to jointly calibrate the scale and shift of shared representations, thereby explicitly modeling client-specific distribution shifts and mitigating the performance degradation caused by global–local representation mismatch. In addition, we introduce a prototype-driven knowledge fusion (PKF) mechanism that reformulates client-specific shifting factors as semantic prototypes to perform cross-client knowledge filtering based on representation similarity. By selectively integrating only semantically consistent and beneficial knowledge, PKF facilitates constructive collaboration while suppressing interference. Extensive experiments on five public benchmarks demonstrate that PFL-AP achieves superior representation alignment and exhibits strong generalizability and robustness in tackling data heterogeneity.