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(FL)是一种有效的分散分布式学习方法,它使多个机构能够在不分享当地数据的情况下联合培训一个模型,但不同购置装置/客户造成的域特征变化大大降低了FL模型的性能。此外,大多数现有FL方法的目的是提高准确性,而不考虑可靠性(例如信心或不确定性),因此,在安全关键应用程序中部署时预测是不可靠的。因此,为了改进FL在非域特征问题上的绩效,同时使模型更加可靠。在本文件中,我们提议建立一个新颖的可信赖的联结迪安特朗宁网络,称为TRFedDis,该网络利用地貌模糊性,以便能够捕捉到全球域内差异性跨客户代表性,并保存本地客户特有的特征学习。与此同时,为了有效地整合分解特性,还引入了不确定性决定组合,以指导网络在证据层面上动态地整合分解特征,同时产生可靠的预测状态。我们拟议的TRFedDDDis, 利用我们拟议的TRF-DDis, 比较性特征的特征,首先是收集全球域域内不易变的可靠性,以展示性能特性为基础,展示另一个域域域域域内不透明性。