LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging
arXiv:2605.20866v1 Announce Type: new Abstract: Communication is a major bottleneck in distributed learning, especially in large-scale settings and in federated learning environments with slow links. Three standard ways to reduce this cost are communication compression, local training, and communication-computation overlap. Methods that combine these ingredients are used in practice and have been found to be effective for large-scale training, but there is little theory for methods that combine all three. We study a heterogeneous-compute setting in which different workers may take different nu
The increasing scale of distributed AI models and the prevalence of federated learning environments with slow or heterogeneous links make communication bottlenecks a critical and timely challenge for AI development.
This research provides theoretical grounding for practical methods that improve the efficiency of distributed AI training by optimizing communication, which is crucial for advancing large-scale AI applications and reducing operational costs.
Theoretically supported methods for overlapping communication and computation in distributed AI will accelerate training times and enable more complex models to be deployed in resource-constrained or decentralized settings.
- · AI developers
- · Cloud providers
- · Federated learning platforms
- · Large-scale AI applications
- · Legacy distributed training methods
- · Data centers with poor network infrastructure
Faster and more cost-effective training of large-scale AI models becomes possible.
This leads to an acceleration in AI development cycles and wider deployment of advanced AI in various industries.
Increased accessibility to advanced AI models could democratize AI, reducing the barrier to entry for smaller organizations and fostering innovation outside of well-resourced labs.
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Read at arXiv cs.LG