Energy-Aware Decentralized Learning with Intermittent Model Training

TL;DR

This paper introduces SkipTrain, an energy-efficient decentralized learning algorithm that strategically skips training rounds to save energy and improve model accuracy, validated by experiments with 256 nodes.

Contribution

SkipTrain is a novel decentralized learning method that reduces energy use by skipping training rounds and enhances accuracy through better model mixing.

Findings

SkipTrain reduces energy consumption by 50%.

SkipTrain increases model accuracy by up to 12%.

Empirical validation with 256 nodes demonstrates effectiveness.

Abstract

Decentralized learning (DL) offers a powerful framework where nodes collaboratively train models without sharing raw data and without the coordination of a central server. In the iterative rounds of DL, models are trained locally, shared with neighbors in the topology, and aggregated with other models received from neighbors. Sharing and merging models contribute to convergence towards a consensus model that generalizes better across the collective data captured at training time. In addition, the energy consumption while sharing and merging model parameters is negligible compared to the energy spent during the training phase. Leveraging this fact, we present SkipTrain, a novel DL algorithm, which minimizes energy consumption in decentralized learning by strategically skipping some training rounds and substituting them with synchronization rounds. These training-silent periods, besides saving energy, also allow models to better mix and finally produce models with superior accuracy than typical DL algorithms that train at every round. Our empirical evaluations with 256 nodes demonstrate that SkipTrain reduces energy consumption by 50% and increases model accuracy by up to 12% compared to D-PSGD, the conventional DL algorithm.