# LiD-FL: Towards List-Decodable Federated Learning Source: [LiD-FL: Towards List-Decodable Federated Learning]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:48:55.522Z Authors: Hong Liu, Liren Shan, Han Bao, Ronghui You, Yuhao Yi, Jiancheng Lv Published: 2024-08-09T09:29:02.000Z ## Abstract Federated learning is often used in environments with many unverified participants. Therefore, federated learning under adversarial attacks receives significant attention. This paper proposes an algorithmic framework for list-decodable federated learning, where a central server maintains a list of models, with at least one guaranteed to perform well. The framework has no strict restriction on the fraction of honest workers, extending the applicability of Byzantine federated learning to the scenario with more than half adversaries. Under proper assumptions on the loss function, we prove a convergence theorem for our method. Experimental results, including image classification tasks with both convex and non-convex losses, demonstrate that the proposed algorithm can withstand the malicious majority under various attacks. ## 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]()