Decentralized Federated Domain Generalization with Style Sharing: A Formal Modeling and Convergence Analysis
Much of the federated learning (FL) literature focuses on settings where local dataset statistics remain the same between training and testing time. Recent advances in domain generalization (DG) aim to use data from source (training) domains to train a model that generalizes well to data from unseen target (testing) domains. In this paper, we are motivated by two major gaps in existing work on FL and DG: (1) the lack of formal mathematical analysis of DG objectives and training processes; and (2) DG research in FL being limited to the conventional star-topology architecture. Addressing the second gap, we develop $\textit{Decentralized Federated Domain Generalization with Style Sharing}$ ($\texttt{StyleDDG}$), a fully decentralized DG algorithm designed to allow devices in a peer-to-peer network to achieve DG based on sharing style information inferred from their datasets. Additionally, we fill the first gap by providing the first systematic approach to mathematically analyzing style-based DG training optimization. We cast existing centralized DG algorithms within our framework, and employ their formalisms to model $\texttt{StyleDDG}$. Based on this, we obtain analytical conditions under which a sub-linear convergence rate of $\texttt{StyleDDG}$ can be obtained. Through experiments on two popular DG datasets, we demonstrate that $\texttt{StyleDDG}$ can obtain significant improvements in accuracy across target domains with minimal added communication overhead compared to decentralized gradient methods that do not employ style sharing.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain GeneralizationFederated LearningSimilar Papers 제목 키워드 기반
Multi-Source Collaborative Style Augmentation and Domain-Invariant Learning for Federated Domain Generalization
Federated domain generalization aims to learn a generalizable model from multiple decentralized source domains for deploying on the unseen target domain. The style augmentation methods have achieved great progress on dom…
Domain GeneralizationCO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID
Federated domain generalization for person re-identification (FedDG-ReID) aims to collaboratively train a pedestrian retrieval model across multiple decentralized source domains such that it can generalize to unseen targ…
Person Re-IdentificationDomain GeneralizationFederated Cross-Modal Style-Aware Prompt Generation
Prompt learning has propelled vision-language models like CLIP to excel in diverse tasks, making them ideal for federated learning due to computational efficiency. However, conventional approaches that rely solely on fin…
Computational EfficiencyImage ClassificationFederated LearningStableFDG: Style and Attention Based Learning for Federated Domain Generalization
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 pr…
DiversityDomain GeneralizationFederated LearningFederated Domain Generalization for Image Recognition via Cross-Client Style Transfer
Domain generalization (DG) has been a hot topic in image recognition, with a goal to train a general model that can perform well on unseen domains. Recently, federated learning (FL), an emerging machine learning paradigm…
Domain GeneralizationFederated LearningStyle Transfer