Communication Efficient Decentralized Learning Over Bipartite Graphs

Chaouki Ben Issaid

doi:10.1109/TWC.2021.3126859

Communication Efficient Decentralized Learning Over Bipartite Graphs

2022-06-01

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DOI：10.1109/TWC.2021.3126859

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In this paper, we propose a communication-efficiently decentralized machine learning framework that solves a consensus optimization problem defined over a network of inter-connected workers. The proposed algorithm, Censored and Quantized Generalized GADMM (CQ-GGADMM), leverages the worker grouping and decentralized learning ideas of Group Alternating Direction Method of Multipliers (GADMM), and pushes the frontier in communication efficiency by extending its applicability to generalized network topologies, while incorporating link censoring for negligible updates after quantization. We theoretically prove that CQ-GGADMM achieves the linear convergence rate when the local objective functions are strongly convex under some mild assumptions. Numerical simulations corroborate that CQ-GGADMM exhibits higher communication efficiency in terms of the number of communication rounds and transmit energy consumption without compromising the accuracy and convergence speed, compared to the censored decentralized ADMM, and the worker grouping method of GADMM.

Keywords:

Convergence

Quantization (signal)

Network topology

Topology

Data models

Stochastic processes

Payloads

Alternating direction method of multipliers

communication efficiency

decentralized machine learning

stochastic quantization

Communication Efficient Decentralized Learning Over Bipartite Graphs

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