StableFDG: Style and Attention Based Learning for Federated Domain Generalization

Jungwuk Park, Shiqiang Wang, Dong Jun Han, Christopher G. Brinton, Jinho Kim, Jaekyun Moon

Research output: Contribution to journalConference articlepeer-review


Traditional federated learning (FL) algorithms operate under the assumption that the data distributions at training (source domains) and testing (target domain) are the same.The fact that domain shifts often occur in practice necessitates equipping FL methods with a domain generalization (DG) capability.However, existing DG algorithms face fundamental challenges in FL setups due to the lack of samples/domains in each client's local dataset.In this paper, we propose StableFDG, a style and attention based learning strategy for accomplishing federated domain generalization, introducing two key contributions.The first is style-based learning, which enables each client to explore novel styles beyond the original source domains in its local dataset, improving domain diversity based on the proposed style sharing, shifting, and exploration strategies.Our second contribution is an attention-based feature highlighter, which captures the similarities between the features of data samples in the same class, and emphasizes the important/common characteristics to better learn the domain-invariant characteristics of each class in data-poor FL scenarios.Experimental results show that StableFDG outperforms existing baselines on various DG benchmark datasets, demonstrating its efficacy.

Original languageEnglish (US)
JournalAdvances in Neural Information Processing Systems
StatePublished - 2023
Externally publishedYes
Event37th Conference on Neural Information Processing Systems, NeurIPS 2023 - New Orleans, United States
Duration: Dec 10 2023Dec 16 2023

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


Dive into the research topics of 'StableFDG: Style and Attention Based Learning for Federated Domain Generalization'. Together they form a unique fingerprint.

Cite this