  Federated learning benefits from cross-training strategies, which enables
models to train on data from distinct sources to improve the generalization
capability. However, the data heterogeneity between sources may lead models to
gradually forget previously acquired knowledge when undergoing cross-training
to adapt to new tasks or data sources. We argue that integrating personalized
and global knowledge to gather information from multiple perspectives could
potentially improve performance. To achieve this goal, this paper presents a
novel approach that enhances federated learning through a cross-training scheme
incorporating multi-view information. Specifically, the proposed method, termed
FedCT, includes three main modules, where the consistency-aware knowledge
broadcasting module aims to optimize model assignment strategies, which
enhances collaborative advantages between clients and achieves an efficient
federated learning process. The multi-view knowledge-guided representation
learning module leverages fused prototypical knowledge from both global and
local views to enhance the preservation of local knowledge before and after
model exchange, as well as to ensure consistency between local and global
knowledge. The mixup-based feature augmentation module aggregates rich
information to further increase the diversity of feature spaces, which enables
the model to better discriminate complex samples. Extensive experiments were
conducted on four datasets in terms of performance comparison, ablation study,
in-depth analysis and case study. The results demonstrated that FedCT
alleviates knowledge forgetting from both local and global views, which enables
it outperform state-of-the-art methods.
