  Federated learning is a privacy-preserving machine learning technique that
learns a shared model across decentralized clients. It can alleviate privacy
concerns of personal re-identification, an important computer vision task. In
this work, we implement federated learning to person re-identification
(FedReID) and optimize its performance affected by statistical heterogeneity in
the real-world scenario. We first construct a new benchmark to investigate the
performance of FedReID. This benchmark consists of (1) nine datasets with
different volumes sourced from different domains to simulate the heterogeneous
situation in reality, (2) two federated scenarios, and (3) an enhanced
federated algorithm for FedReID. The benchmark analysis shows that the
client-edge-cloud architecture, represented by the federated-by-dataset
scenario, has better performance than client-server architecture in FedReID. It
also reveals the bottlenecks of FedReID under the real-world scenario,
including poor performance of large datasets caused by unbalanced weights in
model aggregation and challenges in convergence. Then we propose two
optimization methods: (1) To address the unbalanced weight problem, we propose
a new method to dynamically change the weights according to the scale of model
changes in clients in each training round; (2) To facilitate convergence, we
adopt knowledge distillation to refine the server model with knowledge
generated from client models on a public dataset. Experiment results
demonstrate that our strategies can achieve much better convergence with
superior performance on all datasets. We believe that our work will inspire the
community to further explore the implementation of federated learning on more
computer vision tasks in real-world scenarios.
