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Related Experiment Videos

Sequential Nonlinear Learning for Distributed Multiagent Systems via Extreme Learning Machines.

Nuri Denizcan Vanli, Muhammed O Sayin, Ibrahim Delibalta

    IEEE Transactions on Neural Networks and Learning Systems
    |March 16, 2016
    PubMed
    Summary

    This study introduces a distributed algorithm for online nonlinear learning in multiagent systems. The method enables individual agents to achieve optimal performance comparable to centralized systems, even with local data.

    Related Experiment Videos

    Area of Science:

    • Machine Learning
    • Distributed Systems
    • Neural Networks

    Background:

    • Online nonlinear learning in distributed multiagent systems presents challenges due to localized data access.
    • Each agent typically uses a single hidden layer feedforward neural network (SLFN) to minimize loss functions with its own data.

    Purpose of the Study:

    • To develop a distributed algorithm enabling agents to train SLFNs that match the performance of an optimal centralized system.
    • To provide performance guarantees and analyze convergence rates based on data and network parameters.

    Main Methods:

    • Introduction of a distributed subgradient-based extreme learning machine (ELM) algorithm.
    • Agents exchange information with neighbors to collectively train SLFNs.

    Main Results:

    • Guaranteed upper bounds on the performance of individual agent SLFNs.
    • Demonstration that individual SLFNs asymptotically achieve the performance of the optimal centralized batch SLFN.
    • Analysis distinguishing the impact of data- and network-dependent parameters on convergence.

    Conclusions:

    • The proposed distributed ELM algorithm effectively trains SLFNs in multiagent systems.
    • The algorithm achieves oracle performance faster than existing methods, making it suitable for big data applications.