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Decentralized learning has emerged as an alternative method to the popularparameter-server framework which suffers from high communication burden,single-point failure and scalability issues due to the need of a centralserver. However, most existing works focus on a single shared model for allworkers regardless of the data heterogeneity problem, rendering the resultingmodel performing poorly on individual workers. In this work, we propose a novelpersonalized decentralized learning algorithm named DePRL via sharedrepresentations. Our algorithm relies on ideas from representation learningtheory to learn a low-dimensional global representation collaboratively amongall workers in a fully decentralized manner, and a user-specificlow-dimensional local head leading to a personalized solution for each worker.We show that DePRL achieves, for the first time, a provable linear speedup forconvergence with general non-linear representations (i.e., the convergence rateis improved linearly with respect to the number of workers). Experimentalresults support our theoretical findings showing the superiority of our methodin data heterogeneous environments.

DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations