<ul data-eligibleForWebStory="true"><li>Federated Learning (FL) allows organizations to train machine learning models collectively without sharing raw data, but it can be vulnerable to attacks like Byzantine and backdoor attacks.</li><li>A new solution called FedTruth has been proposed to defend against malicious model updates in FL by estimating a 'ground-truth model update' without the need for a benign root dataset or assumptions on data distribution.</li><li>FedTruth considers contributions from all benign clients and employs dynamic aggregation weights to reduce the impact of poisoned model updates, making it effective against Byzantine and backdoor attacks in large-scale FL systems.</li><li>The proposed FedTruth solution aims to enhance the security of federated learning by addressing vulnerabilities to model poisoning attacks without relying on specific data assumptions or requiring access to a benign root dataset.</li></ul>

Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning (full version)

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