Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning

no code implementations • 21 Nov 2022 • Xueyang Tang, Song Guo, Jie Zhang

Recently, data heterogeneity among the training datasets on the local clients (a. k. a., Non-IID data) has attracted intense interest in Federated Learning (FL), and many personalized federated learning methods have been proposed to handle it.

Out-of-Distribution Generalization Personalized Federated Learning

Personalized Federated Learning with Contextualized Generalization

no code implementations • 24 Jun 2021 • Xueyang Tang, Song Guo, Jingcai Guo

The prevalent personalized federated learning (PFL) usually pursues a trade-off between personalization and generalization by maintaining a shared global model to guide the training process of local models.

Personalized Federated Learning