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.

翻译：联邦学习受益于跨训练策略，该策略使模型能够在不同来源的数据上进行训练，从而提升泛化能力。然而，在跨训练过程中，数据源间的异构性可能导致模型为适应新任务或数据源而逐渐遗忘先前习得的知识。我们认为，整合个性化与全局知识以从多视角汇集信息有望提升性能。为实现这一目标，本文提出一种新颖方法，通过融入多视角信息的跨训练方案增强联邦学习。具体而言，所提方法（称为FedCT）包含三个核心模块：一致性感知知识广播模块旨在优化模型分配策略，从而增强客户端间的协作优势并实现高效的联邦学习流程；多视角知识引导表征学习模块利用全局与局部视角融合的原型知识，以增强模型交换前后局部知识的保持能力，并确保局部与全局知识间的一致性；基于混合的特征增强模块聚合丰富信息以进一步提升特征空间的多样性，使模型能更好地区分复杂样本。我们在四个数据集上进行了性能比较、消融研究、深入分析与案例研究等大量实验。结果表明，FedCT从局部与全局视角均缓解了知识遗忘问题，使其性能优于现有先进方法。