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Collaborative machine learning (CML) techniques, such as federated learning,were proposed to collaboratively train deep learning models using multipleend-user devices and a server. CML techniques preserve the privacy of end-usersas it does not require user data to be transferred to the server. Instead,local models are trained and shared with the server. However, the low resourceutilisation of CML techniques makes the training process inefficient, therebylimiting the use of CML in the real world. Idling resources both on the serverand devices due to sequential computation and communication is the principalcause of low resource utilisation. A novel framework PipeLearn that leveragespipeline parallelism for CML techniques is developed to improve resourceutilisation substantially. A new training pipeline is designed to parallelisethe computations on different hardware resources and communication on differentbandwidth resources, thereby accelerating the training process in CML. Thepipeline is further optimised to ensure maximum utilisation of availableresources. The experimental results confirm the validity of the underlyingapproach of PipeLearn and highlight that when compared to federated learning:(i) the idle time of the server can be reduced by 2.2x - 28.5x, (ii) thenetwork throughput can be increased by 56.6x - 321.3x, and (iii) the overalltraining time can be accelerated by 1.5x - 21.6x under varying networkconditions for two popular convolutional models without sacrificing accuracy.PipeLearn is available for public download fromhttps://github.com/blessonvar/PipeLearn.

PipeLearn: Pipeline Parallelism for Collaborative Machine Learning