FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning

• URL: http://arxiv.org/abs/2508.10042v1
• Date: Mon, 11 Aug 2025 22:12:27 GMT
• Title: FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
• Authors: Jane Carney, Kushal Upreti, Gaby G. Dagher, Tim Andersen,
• Abstract summary: Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data.<n>It ensures privacy for clients, but is susceptible to data poisoning attacks during training that degrade model performance and integrity.<n>We present Sys, a novel blockchain-enabled poison detection framework in federated learning.
• Score: 1.2499537119440243
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data. It ensures privacy for clients, but is susceptible to data poisoning attacks during training that degrade model performance and integrity. Current poisoning detection methods in federated learning lack a standardized detection method or take significant liberties with trust. In this paper, we present \Sys, a novel blockchain-enabled poison detection framework in federated learning. The framework decentralizes the role of the global server across participating clients. We introduce a judge model used to detect data poisoning in model updates. The judge model is produced by each client and verified to reach consensus on a single judge model. We implement our solution to show \Sys is robust against data poisoning attacks and the creation of our judge model is scalable.

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