Poster

Quantized Decentralized Stochastic Learning over Directed Graphs

Hossein Taheri · Aryan Mokhtari · Hamed Hassani · Ramtin Pedarsani

Virtual

Keywords: [ Convex Optimization ] [ Non-convex Optimization ] [ Parallel and Distributed Learning ] [ Optimization - Large Scale, Parallel and Distributed ]

[ Abstract ]
[ Slides
Wed 15 Jul 10 a.m. PDT — 10:45 a.m. PDT
Wed 15 Jul 9 p.m. PDT — 9:45 p.m. PDT

Abstract:

We consider a decentralized stochastic learning problem where data points are distributed among computing nodes communicating over a directed graph. As the model size gets large, decentralized learning faces a major bottleneck that is the heavy communication load due to each node transmitting large messages (model updates) to its neighbors. To tackle this bottleneck, we propose the quantized decentralized stochastic learning algorithm over directed graphs that is based on the push-sum algorithm in decentralized consensus optimization. More importantly, we prove that our algorithm achieves the same convergence rates of the decentralized stochastic learning algorithm with exact-communication for both convex and non-convex losses. Furthermore, our numerical results illustrate significant speed-up compared to the exact-communication methods.

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