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    This study introduces a consensus algorithm for decentralized machine learning, enabling distributed neural networks to achieve performance comparable to centralized models. The method significantly reduces communication costs by ensuring convergence with minimal consensus steps.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Growing interest in large-scale, privacy-preserving machine learning.
    • Challenges in data sharing and centralization due to privacy and computational constraints.
    • Parallel computation topologies (master-slave, decentralized) explored for distributed training.

    Purpose of the Study:

    • Design a consensus algorithm for decentralized machine learning agents.
    • Enable distributed neural networks to achieve performance similar to centralized models.
    • Reduce communication costs in distributed training scenarios.

    Main Methods:

    • Developed a consensus algorithm for agents on a decentralized graph.
    • Analyzed convergence properties of the algorithm on undirected graphs.
    • Simulated distributed training for multi-layer neural networks without data exchange.

    Main Results:

    • Agents on an undirected graph converge to the same optimal model with a single consensus step.
    • Distributed training achieves performance comparable or superior to centralized models.
    • The proposed algorithm effectively mitigates communication bottlenecks in decentralized systems.

    Conclusions:

    • The consensus algorithm facilitates efficient distributed training in privacy-concerned machine learning.
    • Single consensus steps are sufficient for model convergence, significantly reducing communication overhead.
    • Decentralized training without data exchange offers a viable and effective alternative to centralized approaches.