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Communication Dans Un Congrès Année : 2024

Demo: Highlighting the Limits of Federated Learning in Intrusion Detection

Résumé

Federated learning (FL) is a distributed learning paradigm enabling participants to collaboratively train a machine learning (ML) model. In security-oriented tasks, FL can be used to share attack knowledge, without sharing participants’ local data. Recent research results reveal that highly heterogeneous data distributions can prevent federations from converging towards an appropriate global model. Moreover, maintaining trustworthiness is challenging, as FL-based collaborative intrusion detection systems (CIDSs) are vulnerable to malicious updates. In this demonstration paper, we present critical examples of these challenges using a set of standardized public datasets and a dedicated automation tool. We review the impact of heterogeneity using different data-distribution, before looking at a scenario with malicious actors
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Dates et versions

hal-04606824 , version 1 (10-06-2024)

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  • HAL Id : hal-04606824 , version 1

Citer

Léo Lavaur, Yann Busnel, Fabien Autrel. Demo: Highlighting the Limits of Federated Learning in Intrusion Detection. 44th IEEE International Conference on Distributed Computing Systems (ICDCS 2024), IEEE, Jul 2024, Jersey City, NJ, United States. ⟨hal-04606824⟩
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