Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning

TL;DR

This paper introduces MPFT, a federated learning framework that uses multi-domain prototypes for effective domain adaptation, achieving rapid convergence, improved accuracy, and privacy preservation without data sharing.

Contribution

MPFT is a novel prototype-based federated fine-tuning method that enhances domain adaptation and privacy in federated learning with a single communication round.

Findings

MPFT significantly improves in-domain and out-of-domain accuracy.

MPFT converges within a single communication round.

MPFT maintains data privacy through differential privacy.

Abstract

Federated Domain Adaptation (FDA) is a Federated Learning (FL) scenario where models are trained across multiple clients with unique data domains but a shared category space, without transmitting private data. The primary challenge in FDA is data heterogeneity, which causes significant divergences in gradient updates when using conventional averaging-based aggregation methods, reducing the efficacy of the global model. This further undermines both in-domain and out-of-domain performance (within the same federated system but outside the local client). To address this, we propose a novel framework called \textbf{M}ulti-domain \textbf{P}rototype-based \textbf{F}ederated Fine-\textbf{T}uning (MPFT). MPFT fine-tunes a pre-trained model using multi-domain prototypes, i.e., pretrained representations enriched with domain-specific information from category-specific local data. This enables supervised learning on the server to derive a globally optimized adapter that is subsequently distributed to local clients, without the intrusion of data privacy. Empirical results show that MPFT significantly improves both in-domain and out-of-domain accuracy over conventional methods, enhancing knowledge preservation and adaptation in FDA. Notably, MPFT achieves convergence within a single communication round, greatly reducing computation and communication costs. To ensure privacy, MPFT applies differential privacy to protect the prototypes. Additionally, we develop a prototype-based feature space hijacking attack to evaluate robustness, confirming that raw data samples remain unrecoverable even after extensive training epochs. The complete implementation of MPFL is available at \url{https://anonymous.4open.science/r/DomainFL/}.