# Krum Federated Chain (KFC): Using blockchain to defend against adversarial attacks in Federated Learning Source: [Krum Federated Chain (KFC): Using blockchain to defend against adversarial attacks in Federated Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:06:07.870Z Authors: Mario Garc\\'ia-M\\'arquez, Nuria Rodr\\'iguez-Barroso, M.Victoria Luz\\'on, Francisco Herrera Published: 2025-02-10T15:15:50.000Z ## Abstract Federated Learning presents a nascent approach to machine learning, enabling collaborative model training across decentralized devices while safeguarding data privacy. However, its distributed nature renders it susceptible to adversarial attacks. Integrating blockchain technology with Federated Learning offers a promising avenue to enhance security and integrity. In this paper, we tackle the potential of blockchain in defending Federated Learning against adversarial attacks. First, we test Proof of Federated Learning, a well known consensus mechanism designed ad-hoc to federated contexts, as a defense mechanism demonstrating its efficacy against Byzantine and backdoor attacks when at least one miner remains uncompromised. Second, we propose Krum Federated Chain, a novel defense strategy combining Krum and Proof of Federated Learning, valid to defend against any configuration of Byzantine or backdoor attacks, even when all miners are compromised. Our experiments conducted on image classification datasets validate the effectiveness of our proposed approaches. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()