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arXiv · 2203.07802

A Framework for Verifiable and Auditable Federated Anomaly Detection

Gabriele Santin·Inna Skarbovsky·Fabiana Fournier·Bruno Lepri
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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.

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Gabriele Santin, Inna Skarbovsky, Fabiana Fournier, Bruno Lepri. 2022-03-15. A Framework for Verifiable and Auditable Federated Anomaly Detection. https://doi.org/10.1109/access.2022.3196391

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