A Framework for Verifiable and Auditable Federated Anomaly Detection

TL;DR

This paper introduces a federated anomaly detection framework using Random Forests that ensures data privacy, verifiability, and auditability through blockchain integration, enabling collaborative learning on sensitive data.

Contribution

It presents a novel architecture combining federated learning, Random Forests, and blockchain for verifiable anomaly detection without data sharing.

Findings

Effective anomaly detection with privacy preservation

Blockchain ensures verifiable and auditable execution

Framework adaptable to broader ensemble-learning tasks

Abstract

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.