[2604.06795] FedDAP: Domain-Aware Prototype Learning for Federated Learning under Domain Shift

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.06795 (cs) [Submitted on 8 Apr 2026] Title:FedDAP: Domain-Aware Prototype Learning for Federated Learning under Domain Shift Authors:Huy Q. Le, Loc X. Nguyen, Yu Qiao, Seong Tae Kim, Eui-Nam Huh, Choong Seon Hong View a PDF of the paper titled FedDAP: Domain-Aware Prototype Learning for Federated Learning under Domain Shift, by Huy Q. Le and 4 other authors View PDF HTML (experimental) Abstract:Federated Learning (FL) enables decentralized model training across multiple clients without exposing private data, making it ideal for privacy-sensitive applications. However, in real-world FL scenarios, clients often hold data from distinct domains, leading to severe domain shift and degraded global model performance. To address this, prototype learning has been emerged as a promising solution, which leverages class-wise feature representations. Yet, existing methods face two key limitations: (1) Existing prototype-based FL methods typically construct a $\textit{single global prototype}$ per class by aggregating local prototypes from all clients without preserving domain information. (2) Current feature-prototype alignment is $\textit{domain-agnostic}$, forcing clients to align with global prototypes regardless of domain origin. To address these challenges, we propose Federated Domain-Aware Prototypes (FedDAP) to construct domain-specific global prototypes by aggregating local client prototypes within the same d...