<ul><li>Recent advancements in machine learning have led to improved performance but increased computational demands.</li><li>A new paper addresses the threat of Byzantine attacks in federated and distributed setups where compromised clients inject adversarial updates.</li><li>The paper proposes a method that combines trust scores and trial function methodology to filter outliers dynamically, overcoming critical limitations of previous approaches.</li><li>Extensive experiments on synthetic and real ECG data demonstrate the robustness of the proposed methods, which adapt to various scenarios and achieve convergence guarantees comparable to classical algorithms.</li></ul>

Trial and Trust: Addressing Byzantine Attacks with Comprehensive Defense Strategy

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