Privacy-Preserving Object Counting in Lightweight Intelligent Transportation System

In Intelligent Transportation Systems (ITS), tiny machine learning enables resource-efficient on-device learning and deployment of AI models for critical tasks such as traffic flow prediction and pedestrian safety. However, existing object counting models typically consist of a large number of parameters, which require high computational costs that are unsuited for on-device learning. Moreover, these models are usually trained in a centralized manner, which raises data privacy concerns and relies on the unrealistic assumption of independent and identically distributed (IID) data across all vehicles and environments. To address these challenges, we propose a lightweight network, termed Privacy-aware Efficient Generalization Network (PEGNet), that preserves user privacy and improves cross-scenario generalization. Specifically, this network incorporates a Lightweight Adaptive Enhancement (LAE) module with deformable convolutions to efficiently capture multi-scale features under strict resource constraints. We further employ a federated learning framework to collaboratively train the model across distributed IoV nodes with limited local data, thereby significantly reducing per-node computation while safeguarding user data privacy. To enhance the robustness of non-IID data, we introduce a Moment Distance loss (MDloss) that prompts the model to adapt to diverse traffic scenes. Experiments on multiple vehicle and crowd counting benchmarks demonstrate that the proposed method achieves superior efficiency and accuracy.