Communication Costs Analysis of Unsupervised Federated Learning : an Anomaly Detection Scenario

The rapid growth of distributed data across edge devices has prompted the development of decentralized machine learning techniques, such as Federated Learning (FL), to address privacy and data transfer concerns. Only a few recent works have focused on unsupervised FL approaches compared to their supervised counterparts, with the consequence that many aspects of these solutions, e.g., the communication cost, have not been thoroughly investigated. In this paper, we analyse the communication cost associated with unsupervised federated anomaly detection, focusing on a proposed method where clients are grouped into communities based on inlier patterns and subsequently train autoencoder models in a federated fashion. Our analysis quantifies the communication overhead introduced by the federated learning process and compares it to traditional centralized approaches for anomaly detection. We also explore potential trade-offs between communication cost, privacy, and model performance. Our findings reveal that the unsupervised federated approach can achieve a significant reduction in communication cost (up to 83.33%) with comparable performance, by selecting better-suited models. Furthermore, the adjustments we implement render the methodology independent of dataset size, offering notable privacy benefits and competitive accuracy performance, making it highly effective in industrial scenarios with large local datasets and a moderate number of clients.