Dataset Dictionary Learning in a Wasserstein Space for Federated Domain Adaptation

TL;DR

This paper introduces a privacy-preserving decentralized dataset dictionary learning method using Wasserstein barycenters for multi-source domain adaptation, demonstrating superior performance and robustness across visual benchmarks.

Contribution

The paper proposes a novel decentralized MSDA approach leveraging Wasserstein barycenters to model distributional shifts without sharing private data.

Findings

Outperforms existing decentralized MSDA methods on five benchmarks.

Maintains data privacy by keeping barycentric coordinates undisclosed.

Shows increased robustness to client parallelism.

Abstract

Multi-Source Domain Adaptation (MSDA) is a challenging scenario where multiple related and heterogeneous source datasets must be adapted to an unlabeled target dataset. Conventional MSDA methods often overlook that data holders may have privacy concerns, hindering direct data sharing. In response, decentralized MSDA has emerged as a promising strategy to achieve adaptation without centralizing clients' data. Our work proposes a novel approach, Decentralized Dataset Dictionary Learning, to address this challenge. Our method leverages Wasserstein barycenters to model the distributional shift across multiple clients, enabling effective adaptation while preserving data privacy. Specifically, our algorithm expresses each client's underlying distribution as a Wasserstein barycenter of public atoms, weighted by private barycentric coordinates. Our approach ensures that the barycentric coordinates remain undisclosed throughout the adaptation process. Extensive experimentation across five visual domain adaptation benchmarks demonstrates the superiority of our strategy over existing decentralized MSDA techniques. Moreover, our method exhibits enhanced robustness to client parallelism while maintaining relative resilience compared to conventional decentralized MSDA methodologies.