FedHRA: Federated Hard-Reliable Recovery and Fair Aggregation for Person Re-Identification

Federated learning (FL) enables privacy-preserving person re-identification (ReID) without sharing raw pedestrian images. However, severe ReID domain skew causes client-wise performance unfairness: global models often favor easy clients with clear pedestrian appearances, while hard clients affected by occlusion, blur, poor illumination, and complex backgrounds remain under-optimized. Exist- ing fair FL methods typically compensate under-performing clients by increas- ing their aggregation weights or suppressing conflicting updates. Such strategies are unreliable for ReID because hard-client updates contain both useful domain- specific identity cues and noisy visual components. Blindly amplifying them may strengthen noise, whereas aggressively removing conflicts may discard valuable hard-domain information. Fair federated ReID therefore requires reliability-aware protection of hard-client updates rather than uniform compensation. We propose FedHRA, a Federated Hard-Reliable Recovery and Fair Aggregation framework. FedHRA identifies reliable hard clients by jointly measuring client hardness and update reliability, aligns their updates with the easy-client consensus, and selec- tively recovers reliable hard-domain components. Reliability-guided aggregation further prevents the corrected updates from being diluted. Experiments on four ReID benchmarks with ResNet50 and ViT demonstrate that FedHRA improves hard-client retrieval, overall performance, and cross-client fairness.