Accelerating Optimization and Machine Learning through Decentralization

TL;DR

Decentralized optimization can accelerate convergence in machine learning tasks, outperforming centralized methods in iteration efficiency while preserving privacy and scalability.

Contribution

This work demonstrates that decentralization can paradoxically speed up convergence, challenging the view of it as merely a compromise due to communication or privacy constraints.

Findings

Decentralized methods converge faster than centralized ones in logistic regression.

Distributed training of neural networks can outperform centralized training in iteration count.

Decentralization offers a strategic advantage for efficient optimization.

Abstract

Decentralized optimization enables multiple devices to learn a global machine learning model while each individual device only has access to its local dataset. By avoiding the need for training data to leave individual users' devices, it enhances privacy and scalability compared to conventional centralized learning, where all data has to be aggregated to a central server. However, decentralized optimization has traditionally been viewed as a necessary compromise, used only when centralized processing is impractical due to communication constraints or data privacy concerns. In this study, we show that decentralization can paradoxically accelerate convergence, outperforming centralized methods in the number of iterations needed to reach optimal solutions. Through examples in logistic regression and neural network training, we demonstrate that distributing data and computation across multiple agents can lead to faster learning than centralized approaches, even when each iteration is assumed to take the same amount of time, whether performed centrally on the full dataset or decentrally on local subsets. This finding challenges longstanding assumptions and reveals decentralization as a strategic advantage, offering new opportunities for more efficient optimization and machine learning.