Breaking the Memory Wall for Heterogeneous Federated Learning with Progressive Training

arXiv:2404.13349v1 Announce Type: cross
Abstract: This paper presents ProFL, a novel progressive FL framework to effectively break the memory wall. Specifically, ProFL divides the model into different blocks based on its original architecture. Instead of updating the full model in each training round, ProFL first trains the front blocks and safely freezes them after convergence. Training of the next block is then triggered. This process iterates until the training of the whole model is completed. In this way, the memory …

abstract architecture arxiv breaking cs.dc cs.lg federated learning framework memory novel paper training trains type