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A Framework for Verifiable and Auditable Collaborative Anomaly Detection
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OA
AI
DOI：10.1109/ACCESS.2022.3196391.png)
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Abstract
En 中文
Collaborative and Federated Leaning are emerging approaches 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 of 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 collaborative ensemble-learning methods beyond the specific task and architecture discussed in this paper.
Keywords:
Collaboration
Collaborative work
Anomaly detection
Radio frequency
Training
Computer architecture
Task analysis
Algorithm auditing
anomaly detection
blockchain
collaborative learning