Researchers Cut Network Communication Costs for AI Systems by 90 Percent
A new algorithm dramatically reduces the data overhead required for distributed AI systems to coordinate across multiple machines. The breakthrough could lower infrastructure costs for large-scale machine learning deployments and make AI systems more practical for bandwidth-constrained environments like IoT networks and edge computing.
Originaltitel: Distributed Seeking for Fixed Points of Biased Stochastic Operators: A Communication Efficient Approach
This paper investigates the distributed fixed point seeking problem of sum-separable stochastic operators over the multi-agent network. Based on inexact Krasnosel'ski\uı--Mann iterations, the communication-efficient distributed algorithm is proposed under the relaxed growth bias and variance conditions, generalizing traditional unbiased and bounded additive variance assumptions. To enhance communication efficiency, we integrate communication compression and dynamic period skipping techniques, particularly adopting a unified compressor that allows both relative and absolute compression errors. By introducing a surrogate function for general non-contractive and contractive operators, we establish convergence guarantees of the distributed fixed point iteration, achieving among the first theoretical unifications with distributed non-convex optimization algorithms. Finally, numerical simulations validate the effectiveness of the theoretical results.