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Person Re-identification (ReID) has been extensively studied in recent yearsdue to the increasing demand in public security. However, collecting anddealing with sensitive personal data raises privacy concerns. Therefore,federated learning has been explored for Person ReID, which aims to shareminimal sensitive data between different parties (clients). However, existingfederated learning based person ReID methods generally rely on laborious andtime-consuming data annotations and it is difficult to guarantee cross-domainconsistency. Thus, in this work, a federated unsupervised cluster-contrastive(FedUCC) learning method is proposed for Person ReID. FedUCC introduces athree-stage modelling strategy following a coarse-to-fine manner. In detail,generic knowledge, specialized knowledge and patch knowledge are discoveredusing a deep neural network. This enables the sharing of mutual knowledge amongclients while retaining local domain-specific knowledge based on the kinds ofnetwork layers and their parameters. Comprehensive experiments on 8 publicbenchmark datasets demonstrate the state-of-the-art performance of our proposedmethod.

Robust Knowledge Adaptation for Federated Unsupervised Person ReID