  Re-Identification systems (Re-ID) are crucial for public safety but face the
challenge of having to adapt to environments that differ from their training
distribution. Furthermore, rigorous privacy protocols in public places are
being enforced as apprehensions regarding individual freedom rise, adding
layers of complexity to the deployment of accurate Re-ID systems in new
environments. For example, in the European Union, the principles of ``Data
Minimization'' and ``Purpose Limitation'' restrict the retention and processing
of images to what is strictly necessary. These regulations pose a challenge to
the conventional Re-ID training schemes that rely on centralizing data on
servers. In this work, we present a novel setting for privacy-preserving
Distributed Unsupervised Domain Adaptation for person Re-ID (DUDA-Rid) to
address the problem of domain shift without requiring any image transfer
outside the camera devices. To address this setting, we introduce Fed-Protoid,
a novel solution that adapts person Re-ID models directly within the edge
devices. Our proposed solution employs prototypes derived from the source
domain to align feature statistics within edge devices. Those source prototypes
are distributed across the edge devices to minimize a distributed Maximum Mean
Discrepancy (MMD) loss tailored for the DUDA-Rid setting. Our experiments
provide compelling evidence that Fed-Protoid outperforms all evaluated methods
in terms of both accuracy and communication efficiency, all while maintaining
data privacy.
