Differentially private federated learning for image analysis in smart-city surveillance

Abstract

Federated learning combined with differential privacy offers a practical way to train image-analysis models on distributed camera and sensor networks. This combination is most often implemented through DP-SGD, and it avoids centralizing raw visual data. This mini review examines differentially private federated learning (DPFL) in smart-city surveillance, tracing the method from the original DP-SGD formulation to newer, high-accuracy variants, and examining the gradient-leakage threat DPFL is intended to mitigate. We draw on more than 60 sources. These span federated-learning architectures, differential-privacy mechanisms, gradient-inversion attacks, and image-centric applications in surveillance, remote sensing, and IoT-enabled smart cities. Together, they show that DPFL is a credible, rapidly maturing framework rather than a finished solution. Across published CIFAR-10 results at a fixed privacy budget, the gap between private and non-private image classification has narrowed substantially. It fell from about 22 percentage points in 2016 to roughly 13–14 points by 2022. Applied smart-city systems now combine DP with secure aggregation, homomorphic encryption, or adaptive noise. These systems report accuracies above 90%, with reduced communication overhead. The review concludes by identifying the main barriers to moving DPFL from curated benchmarks and hybrid pilot systems to heterogeneous, real-world camera networks.