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 trusted federated disentangling network, termed TrFedDis, 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 TrFedDis 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 TrFedDis provides outstanding performance with a high degree of reliability as compared to other state-of-the-art FL approaches.

翻译：联邦学习作为一种高效的分布式学习方法，使多个机构能够在不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严重降低了联邦学习模型的性能。此外，现有联邦学习方法大多旨在提升准确率而未考虑可靠性（如置信度或不确定性），因此在部署到安全关键型应用时预测结果不可靠。为解决非域特征问题并提升联邦学习性能，同时增强模型可靠性，本文提出一种新颖的信任联邦解耦网络TrFedDis。该网络利用特征解耦技术，使模型既能捕获全局域无关的跨客户端表示，又能保留本地客户端特异性特征学习。同时，为有效整合解耦特征，我们引入不确定性感知决策融合方法，指导网络在证据层面动态融合解耦特征，并生成含估计不确定性的可靠预测。据我们所知，TrFedDis是首个将基于证据不确定性的联邦学习方法与特征解耦相结合的工作，显著提升了联邦学习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，与现有最优联邦学习方法相比，本文提出的TrFedDis在保持高可靠性的同时实现了卓越性能。