A Framework for Verifiable and Auditable Federated Anomaly Detection
This work addresses the challenge of collaborative anomaly detection for domains with sensitive data and rare events, offering a verifiable framework that could extend to broader federated ensemble-learning methods.
The paper tackles the problem of federated anomaly detection by proposing a novel algorithmic architecture using Random Forests to enable accurate classifiers with privacy-preserving insight-sharing, and integrates it with blockchain for verifiable and auditable execution.
Federated Leaning is an emerging approach to manage cooperation between a group of agents for the solution of Machine Learning tasks, with the goal of improving each agent's performance without disclosing any data. In this paper we present a novel algorithmic architecture that tackle this problem in the particular case of Anomaly Detection (or classification or rare events), a setting where typical applications often comprise data with sensible information, but where the scarcity of anomalous examples encourages collaboration. We show how Random Forests can be used as a tool for the development of accurate classifiers with an effective insight-sharing mechanism that does not break the data integrity. Moreover, we explain how the new architecture can be readily integrated in a blockchain infrastructure to ensure the verifiable and auditable execution of the algorithm. Furthermore, we discuss how this work may set the basis for a more general approach for the design of federated ensemble-learning methods beyond the specific task and architecture discussed in this paper.