Tackling non-IID data is an open challenge in federated learning research. Existing FL methods, including robust FL and personalized FL, are designed to improve model performance without consideration of interpreting non-IID across clients. This paper aims to design a novel FL method to robust and interpret the non-IID data across clients. Specifically, we interpret each client's dataset as a mixture of conceptual vectors that each one represents an interpretable concept to end-users. These conceptual vectors could be pre-defined or refined in a human-in-the-loop process or be learnt via the optimization procedure of the federated learning system. In addition to the interpretability, the clarity of client-specific personalization could also be applied to enhance the robustness of the training process on FL system. The effectiveness of the proposed method have been validated on benchmark datasets.

翻译：解决非独立同分布数据是联邦学习研究中的一个开放挑战。现有的联邦学习方法，包括鲁棒联邦学习和个性化联邦学习，旨在提升模型性能，而未考虑解释客户端间的非独立同分布性。本文旨在设计一种新颖的联邦学习方法，以鲁棒地解释客户端间的非独立同分布数据。具体而言，我们将每个客户端的数据集解释为概念向量的混合，每个向量代表一个对终端用户可解释的概念。这些概念向量可以预先定义，或通过人机交互流程进行优化，亦可在联邦学习系统的优化过程中学习得到。除可解释性外，客户端特定个性化的清晰度也可用于增强联邦学习系统训练过程的鲁棒性。所提方法的有效性已在基准数据集上得到验证。