Federated learning has gained popularity as a solution to data availability and privacy challenges in machine learning. However, the aggregation process of local model updates to obtain a global model in federated learning is susceptible to malicious attacks, such as backdoor poisoning, label-flipping, and membership inference. Malicious users aim to sabotage the collaborative learning process by training the local model with malicious data. In this paper, we propose a novel robust aggregation approach based on recursive Euclidean distance calculation. Our approach measures the distance of the local models from the previous global model and assigns weights accordingly. Local models far away from the global model are assigned smaller weights to minimize the data poisoning effect during aggregation. Our experiments demonstrate that the proposed algorithm outperforms state-of-the-art algorithms by at least $5\%$ in accuracy while reducing time complexity by less than $55\%$. Our contribution is significant as it addresses the critical issue of malicious attacks in federated learning while improving the accuracy of the global model.

本文讨论了联邦学习中的恶意攻击问题，并提出了一种基于递归欧几里得距离计算的新的强大聚合方法，该方法根据局部模型距离上一个全局模型的距离来分配权重，以便减小数据中毒的效应。实验证明，该算法在准确性方面优于最先进的算法至少5％，同时将时间复杂度降低不到55％。

递归欧几里得距离基于鲁棒性聚合技术的联邦学习