| Autor: |
Ben Issaid, C. (Chaouki), Elgabli, A. (Anis), Bennis, M. (Mehdi) |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2022 IEEE Wireless Communications and Networking Conference (WCNC). |
| DOI: |
10.1109/wcnc51071.2022.9771559 |
| Popis: |
In this paper, we propose a communication-efficient alternating direction method of multipliers (ADMM)-based algorithm for solving a distributed learning problem in the stochastic non-convex setting. Our approach runs a few stochastic gradient descent (SGD) steps to solve the local problem at each worker instead of finding the exact/approximate solution as proposed by existing ADMM-based works. By doing so, the proposed framework strikes a good balance between the computation and communication costs. Extensive simulation results show that our algorithm significantly outperforms existing stochastic ADMM in terms of communication-efficiency, notably in the presence of non-independent and identically distributed (non-IID) data. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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