  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.
