FedSPC: Shared Parameter Correction for Personalized Federated Learning
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Abstract (arXiv)
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 of a given PFL method, while leaving personalized parameters unchanged. It can be integrated into three common PFL settings: shared feature extractors, shared classifiers, and fully shared models with local regularization. Experiments on CIFAR-100 and Tiny-ImageNet with ViT, ResNet-34, and VGG-11 show that FedSPC improves performance across representative PFL methods, including FedPer, FedRep, FedBABU, LG-FedAvg, and Ditto.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 38 references that are in the map. First posted on arXiv 11 Jun 2026.
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References · 32
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20 more
- 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
- 2020Personalized Federated Learning with Moreau Envelopescited by 618
- 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
- 2021Federated Learning Based on Dynamic Regularizationcited by 562
- 2019Federated Learning with Personalization Layerscited by 541
- 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
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- 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
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- 2021Layer-wise Adaptive Model Aggregation for Scalable Federated Learningcited by 36
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