Domain-Aware Suppression and Aggregation for Federated DG ReID
Zhixi Yu, Wei Liu, Wenke Huang, Bin Yang, Qian Bie, Guancheng Wan, Xin Xu
Abstract
Federated domain generalization in person re-identification (FedDG-ReID) aims to learn a privacy-preserving server model from decentralized client source domains that generalizes to unseen domains. Existing approaches enhance the generalizability of the server model by increasing the diversity of client person data. However, these methods overlook that ReID model parameters are easily biased by client-specific data distributions, leading to the capture of excessive domain-specific identity information. Such identity information (e.g., clothing style) struggles with identity information in unseen domains, thereby hindering the generalization ability of the server model. To address this, we propose a novel FedDG-ReID framework, which mainly consists of Domain-aware Parameter Suppression (DPS) and Domain-invariant Weighted Aggregation (DWA), called FedSupWA. Specifically, DPS adaptively attenuates the update magnitude of the parameters based on the fit of the parameters to the client
BibTeX
@inproceedings{aaai2026_domainawaresuppr,
title = {Domain-Aware Suppression and Aggregation for Federated DG ReID},
author = {Zhixi Yu and Wei Liu and Wenke Huang and Bin Yang and Qian Bie and Guancheng Wan and Xin Xu},
booktitle = {AAAI 2026},
year = {2026}
}