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LightCam: Lightweight Secure Collaborative Learning and Resource-Aware Aggregation in Camera Networks
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DOI：10.1007/s10723-026-09835-8.png)
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Abstract
En 中文
LightCam is a lightweight, privacy-preserving federated learning framework for resource-constrained camera networks in smart city surveillance. It targets three key challenges: strict protection of identity-bearing video and audio, limited computation and bandwidth at edge cameras, and heterogeneous data and system conditions. LightCam decomposes a multimodal model into a frozen backbone and a small task head, so cameras only train and upload the head, greatly reducing local cost and exposure of shallow features. Local data are split into non-sensitive anchor samples and privacy-critical samples; DP-SGD is applied only to the private subset with a scene-aware dynamic privacy budget. A resource-aware aggregation mechanism further weights client updates by data volume, update stability, and device status. Experiments on a multimodal benchmark constructed from CelebA, CIFAR-10, and UrbanSound8K show that LightCam achieves 85.82% accuracy under a strict total privacy budget ( $$\varepsilon =2.0$$ ), while reducing per-round communication by over two orders of magnitude and maintaining stable convergence under heterogeneous and impaired clients.
Keywords:
Lightweight
Camera networks
Federated learning
Security IoT
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