PiPar: Pipeline Parallelism for Collaborative Machine Learning

TL;DR

PiPar introduces pipeline parallelism to collaborative machine learning, significantly reducing idle times and accelerating training without sacrificing accuracy, especially under varying network conditions and privacy constraints.

Contribution

This paper presents PiPar, a novel framework that applies pipeline parallelism to improve resource utilization and training speed in collaborative machine learning.

Findings

Server idle time reduced by up to 64.1x

Training time accelerated by up to 34.6x

Effective under privacy, heterogeneous devices, and changing bandwidths

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 communication as the principal cause of low resource utilization. A novel framework PiPar that leverages pipeline parallelism for CML techniques is developed to substantially improve resource utilization. A new training pipeline is designed to parallelize the computations on different hardware resources and communication on different bandwidth resources, thereby accelerating the training process in CML. A low overhead automated parameter selection method is proposed to optimize the pipeline, maximizing the utilization of available resources. The experimental results confirm the validity of the underlying approach of PiPar and highlight that when compared to federated learning: (i) the idle time of the server can be reduced by up to 64.1x, and (ii) the overall training time can be accelerated by up to 34.6x under varying network conditions for a collection of six small and large popular deep neural networks and four datasets without sacrificing accuracy. It is also experimentally demonstrated that PiPar achieves performance benefits when incorporating differential privacy methods and operating in environments with heterogeneous devices and changing bandwidths.