As a distributed learning, Federated Learning (FL) faces two challenges: the unbalanced distribution of training data among participants, and the model attack by Byzantine nodes. In this paper, we consider the long-tailed distribution with the presence of Byzantine nodes in the FL scenario. A novel two-layer aggregation method is proposed for the rejection of malicious models and the advisable selection of valuable models containing tail class data information. We introduce the concept of think tank to leverage the wisdom of all participants. Preliminary experiments validate that the think tank can make effective model selections for global aggregation.

翻译：作为一种分布式学习方法，联邦学习面临两大挑战：参与者之间训练数据分布不均衡以及拜占庭节点的模型攻击。本文考虑联邦学习场景中同时存在长尾数据分布与拜占庭节点的问题，提出了一种新颖的两层聚合方法，用于剔除恶意模型并明智地选择包含尾类数据信息的价值模型。我们引入"智库"概念以汇聚所有参与者的智慧。初步实验验证了该智库能够为全局聚合做出有效的模型选择。