  In this paper, we study the problem of federated domain generalization
(FedDG) for person re-identification (re-ID), which aims to learn a generalized
model with multiple decentralized labeled source domains. An empirical method
(FedAvg) trains local models individually and averages them to obtain the
global model for further local fine-tuning or deploying in unseen target
domains. One drawback of FedAvg is neglecting the data distributions of other
clients during local training, making the local model overfit local data and
producing a poorly-generalized global model. To solve this problem, we propose
a novel method, called "Domain and Feature Hallucinating (DFH)", to produce
diverse features for learning generalized local and global models.
Specifically, after each model aggregation process, we share the Domain-level
Feature Statistics (DFS) among different clients without violating data
privacy. During local training, the DFS are used to synthesize novel domain
statistics with the proposed domain hallucinating, which is achieved by
re-weighting DFS with random weights. Then, we propose feature hallucinating to
diversify local features by scaling and shifting them to the distribution of
the obtained novel domain. The synthesized novel features retain the original
pair-wise similarities, enabling us to utilize them to optimize the model in a
supervised manner. Extensive experiments verify that the proposed DFH can
effectively improve the generalization ability of the global model. Our method
achieves the state-of-the-art performance for FedDG on four large-scale re-ID
benchmarks.
