Who Trains What? Disentangled Global Model Modularization for Federated Learning Guided by Client Metadata

Federated Learning (FL) benefits from numerous client participations, yet resource-constrained clients with sufficient data may be excluded due to straggler effects. We study which parts should each client train given its local data and computation budget? This is difficult because the same model structure in a global model produces overlapping responses to different data characteristics, while one data characteristic may involve many structures. We propose MDMP, a metadata-guided method for disentangled global model partitioning. MDMP measures the responses of model structures to different data characteristics. It then groups structures with similar responses into functional modules and selects for each client a set that covers its major data characteristics within its training budget. Experiments on MNIST, CelebA, and CIFAR-10 show that, under a 95% target computation budget, MDMP reduces measured computation by 6.38–9.24% with at most a 0.94 percentage-point drop in mean client accuracy.