PiPar: Pipeline Parallelism for Collaborative Machine Learning

arXiv:2302.12803v2 Announce Type: replace-cross
Abstract: Collaborative machine learning (CML) techniques, such as federated learning, have been proposed to train deep learning models across multiple mobile devices and a server. CML techniques are privacy-preserving as a local model that is trained on each device instead of the raw data from the device is shared with the server. However, CML training is inefficient due to low resource utilization. We identify idling resources on the server and devices due to sequential computation and …

abstract arxiv collaborative cs.dc cs.lg data deep learning device devices federated learning machine machine learning mobile mobile devices multiple pipeline privacy raw raw data replace server train type