  The increasingly stringent data privacy regulations limit the development of
person re-identification (ReID) because person ReID training requires
centralizing an enormous amount of data that contains sensitive personal
information. To address this problem, we introduce federated person
re-identification (FedReID) -- implementing federated learning, an emerging
distributed training method, to person ReID. FedReID preserves data privacy by
aggregating model updates, instead of raw data, from clients to a central
server. Furthermore, we optimize the performance of FedReID under statistical
heterogeneity via benchmark analysis. We first construct a benchmark with an
enhanced algorithm, two architectures, and nine person ReID datasets with large
variances to simulate the real-world statistical heterogeneity. The benchmark
results present insights and bottlenecks of FedReID under statistical
heterogeneity, including challenges in convergence and poor performance on
datasets with large volumes. Based on these insights, we propose three
optimization approaches: (1) We adopt knowledge distillation to facilitate the
convergence of FedReID by better transferring knowledge from clients to the
server; (2) We introduce client clustering to improve the performance of large
datasets by aggregating clients with similar data distributions; (3) We propose
cosine distance weight to elevate performance by dynamically updating the
weights for aggregation depending on how well models are trained in clients.
Extensive experiments demonstrate that these approaches achieve satisfying
convergence with much better performance on all datasets. We believe that
FedReID will shed light on implementing and optimizing federated learning on
more computer vision applications.
