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arXiv · 2506.00961

Enhancing Parallelism in Decentralized Stochastic Convex Optimization

Ofri Eisen·Ron Dorfman·Kfir Y. Levy
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Abstract

Decentralized learning has emerged as a powerful approach for handling large datasets across multiple machines in a communication-efficient manner. However, such methods often face scalability limitations, as increasing the number of machines beyond a certain point negatively impacts convergence rates. In this work, we propose Decentralized Anytime SGD, a novel decentralized learning algorithm that significantly extends the critical parallelism threshold, enabling the effective use of more machines without compromising performance. Within the stochastic convex optimization (SCO) framework, we establish a theoretical upper bound on parallelism that surpasses the current state-of-the-art, allowing larger networks to achieve favorable statistical guarantees and closing the gap with centralized learning in highly connected topologies.

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BibTeXRIS

Ofri Eisen, Ron Dorfman, Kfir Y. Levy. 2025-06-01. Enhancing Parallelism in Decentralized Stochastic Convex Optimization. https://arxiv.org/abs/2506.00961

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