FedSPC: Shared Parameter Correction for Personalized Federated Learning
Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation. Many PFL methods split the model into shared and personalized parameters, which are jointly trained on each client. However, this creates an optimization issue: shared parameters are updated by clients optimizing different local objectives, which can lead to inconsistent shared updates and weaken the shared representation. To address this problem, we propose Federated Shared Parameter Correction (FedSPC), a modular correction method for PFL. FedSPC applies control-variate correction only to the shared parameters ...
| Authors | Kannanthodath Induchoodan Ajay Menon, Christian Prehofer, Yunfei Xu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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