Rethinking Fairness Evaluation in Federated ReID: Beyond Performance Variance under Domain Skew

Fair federated ReID aims to achieve equitable identity retrieval across decen- tralized and heterogeneous ReID domains. However, when fairness is evaluated solely by performance parity among domains, smaller retrieval performance vari- ance is regarded as stronger fairness. Used alone, this criterion overlooks a fun- damental property of ReID scenarios: different domains inherently possess dif- ferent retrieval difficulties caused by variations in cameras, viewpoints, back- grounds, illumination, and identity distributions. Consequently, equal raw perfor- mance does not necessarily imply equal reference-relative attainment, while low variance can coexist with uniformly poor performance. In this work, we rethink the fairness objective of fair federated ReID and argue that fair achievement should be evaluated relative to each domain’s protocol-matched single-domain reference rather than raw performance disparity alone. To this end, we introduce Single- Domain Supervised Reference (SDSR), a reference calibration scheme that cali- brates federated ReID performance using protocol-matched single-domain refer- ence performance. Based on normalized domain achievement, we further propose SDSR-HM, which adopts harmonic aggregation to emphasize balanced reference- relative attainment across heterogeneous and challenging ReID domains. Exten- sive experiments demonstrate that raw dispersion alone can produce mislead- ing fair-achievement rankings and fail to distinguish high attainment from uni- form under-attainment. Our results reveal the necessity of considering reference- relative attainment in fair federated ReID and provide a more informative criterion for measuring fair achievement under domain skew.