FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction. (arXiv:2212.01548v1 [cs.LG])

Most cross-device federated learning (FL) studies focus on the
model-homogeneous setting where the global server model and local client models
are identical. However, such constraint not only excludes low-end clients who
would otherwise make unique contributions to model training but also restrains
clients from training large models due to on-device resource bottlenecks. In
this work, we propose FedRolex, a partial training (PT)-based approach that
enables model-heterogeneous FL and can train a global server model larger than
the largest client model. …

federated learning