Theoretically Optimal Distributed Anomaly Detection

A novel general framework for distributed anomaly detection with theoretical performance guarantees is proposed. Our algorithmic approach combines existing anomaly detection procedures with a novel method for computing global statistics using local sufficient statistics. Under a Gaussian assumption, our distributed algorithm is guaranteed to perform as well as its centralized counterpart, a condition we call `zero information loss´. We further report experimental results on synthetic as well as real-world data to demonstrate the viability of our approach.