Federated learning (FL), as an effective decentralized distributed learning approach, enables multiple institutions to jointly train a model without sharing their local data. However, the domain feature shift caused by different acquisition devices/clients substantially degrades the performance of the FL model. Furthermore, most existing FL approaches aim to improve accuracy without considering reliability (e.g., confidence or uncertainty). The predictions are thus unreliable when deployed in safety-critical applications. Therefore, aiming at improving the performance of FL in non-Domain feature issues while enabling the model more reliable. In this paper, we propose a novel reliable federated disentangling network, termed RFedDis, which utilizes feature disentangling to enable the ability to capture the global domain-invariant cross-client representation and preserve local client-specific feature learning. Meanwhile, to effectively integrate the decoupled features, an uncertainty-aware decision fusion is also introduced to guide the network for dynamically integrating the decoupled features at the evidence level, while producing a reliable prediction with an estimated uncertainty. To the best of our knowledge, our proposed RFedDis is the first work to develop an FL approach based on evidential uncertainty combined with feature disentangling, which enhances the performance and reliability of FL in non-IID domain features. Extensive experimental results show that our proposed RFedDis provides outstanding performance with a high degree of reliability as compared to other state-of-the-art FL approaches.

翻译：联邦学习（FL）作为一种有效的分布式学习方法，使多个机构能够在无需共享本地数据的情况下联合训练模型。然而，不同采集设备/客户端导致的域特征偏移会显著降低FL模型的性能。此外，现有大多数FL方法旨在提高准确率，而未考虑可靠性（例如置信度或不确定性），因此在部署于安全关键型应用时，其预测结果不可靠。针对上述问题，本文提出一种名为RFedDis的新型可靠联邦解耦网络，通过特征解耦技术同时实现全局域不变跨客户端表征的捕获与本地客户端特定特征学习的保留。同时，为有效整合解耦后的特征，我们引入不确定性感知决策融合机制，在证据层面指导网络动态整合解耦特征，并生成带有不确定性估计的可靠预测。据我们所知，所提出的RFedDis是将基于证据不确定性的方法与特征解耦相结合的首个FL方法，有效提升了FL在非独立同分布域特征场景下的性能与可靠性。大量实验结果表明，与现有最优FL方法相比，RFedDis在保持高可靠性的同时展现出卓越性能。