Accelerating Optimization and Machine Learning via Decentralization

TMLR Paper6837 Authors

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 have 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.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Sai_Aparna_Aketi1

Submission Number: 6837