This paper explores the use of server learning for enhancing the robustness of federated learning against malicious attacks even when clients' training data are not independent and identically distributed. We propose a heuristic algorithm that uses server learning and client update filtering in combination with geometric median aggregation. We demonstrate via experiments that this approach can achieve significant improvement in model accuracy even when the fraction of malicious clients is high, even more than $50\%$ in some cases, and the dataset utilized by the server is small and could be synthetic with its distribution not necessarily close to that of the clients' aggregated data.

翻译：本文探讨了利用服务器学习来增强联邦学习在面对恶意攻击时的鲁棒性，即使客户端训练数据并非独立同分布。我们提出了一种启发式算法，该算法结合了服务器学习、客户端更新过滤以及几何中位数聚合。实验表明，即便恶意客户端比例很高（某些情况下甚至超过50%），且服务器所用数据集较小、可能是合成数据，其分布不必与客户端聚合数据的分布接近，该方法仍能显著提升模型准确率。