arXiv:2606.04757v1 Announce Type: cross Abstract: We study decentralized stochastic smooth convex optimization, where $M$ workers minimize an average objective using local stochastic gradients and neighbor-only communication over a fixed gossip network. A central question in this setting is to determine the largest number of workers that can be used under a total budget of $N$ gradient samples while still preserving the centralized $O(1/\sqrt N)$ statistical rate. We introduce an accelerated decentralized method that preserves this rate for up to $\smash{M\lesssim \sqrt{\rho}\,N^{3/4}}$ worker
This research provides a theoretical advancement in decentralized optimization, pushing the boundaries of efficient large-scale machine learning, aligning with the ongoing trend towards distributed AI systems.
Improved decentralized optimization methods enable more efficient and scalable training of AI models across distributed networks, which is crucial for handling massive datasets and privacy concerns.
The proposed method demonstrates that centralized performance rates can be preserved with a significantly larger number of workers, potentially enabling more robust and parallelized AI training.
- · AI developers
- · Cloud computing providers
- · Large enterprises with distributed data
Increased efficiency and scalability for training large-scale AI models in distributed environments.
Reduced communication overhead and potentially lower energy consumption for training certain types of neural networks.
Acceleration of edge AI and federated learning applications due to more robust decentralized optimization.
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Read at arXiv cs.LG