FedSimRep: A Federated Learning Method Based on Data Representation Selection

As an emerging distributed technology, Federated learning enables model training while protecting privacy without sharing local data. However, the heterogeneity of geographic locations and devices leads to response delays from stragglers. Client grouping in federated learning can tolerate stragglers from a global perspective, but differences in data distribution within groups may result in local performance bias. To address this issue, we propose a federated learning method called FedSimRep, which selects representative clients based on data distribution similarity. FedSimRep performs group training by collecting readiness signals from topological communications and maintains synchronization tables and graphs to track training progress. Additionally, it selects representative clients by leveraging the relationship between local optimization similarity and data distribution similarity to optimize the global model. Experiments and theoretical analyses on public datasets such as MNIST, CIFAR-10, and UEU-Fed demonstrate that FedSimRep achieves significant advantages in terms of accuracy and efficiency.