Detection and Mitigation Data Poisoning Attacks in Multimodal Online Federated Learning

Summary

This paper presents the first systematic study of data poisoning attacks in Multimodal Online Federated Learning (MMO-FL), a setting where IoT edge devices train models in a decentralized, real-time way across multiple modalities. The authors give a theoretical analysis of how such attacks degrade learning performance, then propose a detection and mitigation algorithm for MMO-FL systems. Experiments on the UCI-HAR and USC-HAD multimodal datasets show the approach detects and mitigates the attacks.

Topics

• Adversarial machine learning

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