(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The growing deployment of multi-camera surveillance and analytics systems across urban, commercial, and institutional environments has amplified both the utility and the risks of person re-identification. Centralizing visual data from distributed camera networks poses severe privacy threats, regulatory burdens, and single points of failure, while purely localized models suffer from degraded performance due to limited data diversity. Federated learning offers a decentralized paradigm that enables collaborative model training without raw data exchange, yet conventional federated re-identification still transmits deep features that may inadvertently encode sensitive personal attributes. This paper presents a system-level framework for federated multi-camera person re-identification that integrates privacy-preserving feature decoupling mechanisms. Within each camera edge node, a deep representation is decomposed into identity-discriminative components that are invariant to transient appearance, clothing, and camera-specific biases, and into a complementary set of non-identity attributes that remain local. Only the identity-specific embeddings are encrypted and aggregated across the federated network, while the sensitive part is discarded or stored solely for local inference. The paper examines the architecture, communication topology, and adversarial disentanglement strategies that underpin this decoupling, and it critically analyzes structural trade-offs among accuracy, communication efficiency, fairness, and robustness. Further, it addresses deployment considerations at scale, governance frameworks that align with data protection regulations such as GDPR, and long-term sustainability aspects including energy consumption, continual adaptation, and resilience against distributional shifts. The analysis highlights how feature decoupling reconfigures the privacy-utility frontier and establishes a principled foundation for accountable, transparent, and auditable person re-identification infrastructures. The discussion also identifies open challenges related to heterogeneous camera capabilities, adversarial evasion, and the need for standardized benchmarks that evaluate both re-identification accuracy and privacy leakage under federated constraints. By weaving together architectural design, policy implications, and infrastructure sustainability, this work provides a comprehensive systems perspective on the next generation of privacy-conscious visual intelligence networks.