Distributed Learning for Privacy-Preserving Semi-Supervised Video Anomaly Detection

Semi-supervised video anomaly detection (SS-VAD) is essential for intelligent monitoring. However, collecting large-scale surveillance videos from various organizations raises significant privacy concerns regarding sensitive information. Federated learning offers a promising solution by enabling distributed learning among multiple participants while safeguarding privacy. Despite its potential, research on applying federated learning to SS-VAD remains unexplored due to the inherent challenges of this task. In this paper, we solve this task via proposing DLPP, a novel distributed learning framework for privacy-preserving SS-VAD. It addresses the issue of statistical heterogeneity among data from different participants in real-world federated SS-VAD applications, particularly focusing on non-independent and identically distributed (non-IID) data and imbalanced data volumes. In specific, it addresses these challenges in two key innovations: 1) For the non-IID data challenge, it dynamically updates the client model based on the overall gradient at the client of the previous training round and the degree of divergence between the server model and the client model. In this way, it can better adapt the server model to each client and promote convergence. 2) For the imbalanced data volumes challenge, it adaptively allocates client aggregation weights by comprehensively considering the data volumes, model quality, and learning efficiency of clients. This means a more robust server model can be obtained, and model bias reduced. We conduct extensive experiments to evaluate the performance of DLPP on benchmark datasets by partitioning data to simulate various degrees of non-IID environments. The results show that DLPP significantly outperforms both Baseline and SOTA methods, achieving up to a 3.89% improvement, and its communication efficiency is 3x better than FedAvg.