Federated Learning (FL) enables a group of clients to collaboratively train a model without sharing individual data, but its performance drops when client data are heterogeneous. Clustered FL tackles this by grouping similar clients. However, existing clustered FL approaches rely solely on either data similarity or gradient similarity; however, this results in an incomplete assessment of client similarities. Prior clustered FL approaches also restrict knowledge and representation sharing to clients within the same cluster. This prevents cluster models from benefiting from the diverse client population across clusters. To address these limitations, FedDAG introduces a clustered FL framework, FedDAG, that employs a weighted, class-wise similarity metric that integrates both data and gradient information, providing a more holistic measure of similarity during clustering. In addition, FedDAG adopts a dual-encoder architecture for cluster models, comprising a primary encoder trained on its own clients' data and a secondary encoder refined using gradients from complementary clusters. This enables cross-cluster feature transfer while preserving cluster-specific specialization. Experiments on diverse benchmarks and data heterogeneity settings show that FedDAG consistently outperforms state-of-the-art clustered FL baselines in accuracy.

翻译：联邦学习（FL）使得一组客户端能够在不共享个体数据的情况下协作训练模型，但当客户端数据异构时，其性能会下降。聚类联邦学习通过将相似客户端分组来解决此问题。然而，现有的聚类联邦学习方法仅依赖数据相似性或梯度相似性，这导致对客户端相似性的评估不够全面。以往的聚类联邦学习方法还将知识与表征共享限制在同一集群内的客户端之间，这阻碍了集群模型从跨集群的多样化客户端群体中获益。为应对这些局限性，FedDAG提出了一种聚类联邦学习框架，该框架采用一种加权的、按类别划分的相似性度量，整合了数据与梯度信息，从而在聚类过程中提供更全面的相似性衡量。此外，FedDAG为集群模型采用了一种双编码器架构，包括一个基于自身客户端数据训练的主编码器，以及一个利用来自互补集群的梯度进行精炼的辅助编码器。这使得跨集群特征迁移成为可能，同时保持了集群特定的专长。在多种基准测试和数据异构设置下的实验表明，FedDAG在准确率上持续优于最先进的聚类联邦学习基线方法。