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Route-and-Aggregate Decentralized Federated Learning Under Communication Errors.

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    Route-and-aggregate (R&A) decentralized federated learning (D-FL) improves model training accuracy by efficiently routing data through established paths. This method outperforms traditional gossip protocols, especially in networks with non-participating nodes.

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    Area of Science:

    • Machine Learning
    • Distributed Systems
    • Network Communications

    Background:

    • Decentralized federated learning (D-FL) offers scalable, local model aggregation.
    • Existing D-FL methods often use inefficient gossip protocols, particularly when network nodes are not all D-FL clients.

    Purpose of the Study:

    • Introduce a novel D-FL strategy, route-and-aggregate (R&A) D-FL.
    • Analyze the impact of routing and communication errors on R&A D-FL convergence.
    • Demonstrate the effectiveness of R&A D-FL compared to existing methods.

    Main Methods:

    • Developed R&A D-FL, which uses established routes for model exchange instead of flooding.
    • Incorporated adaptive normalization of aggregation coefficients to handle communication errors.
    • Analyzed convergence properties based on end-to-end packet error rates (PERs).

    Main Results:

    • R&A D-FL demonstrated a 35% improvement in training accuracy over flooding-based D-FL in a ten-client network.
    • Convergence is optimal when using routes with minimal end-to-end packet error rates.
    • With increased routing nodes, R&A D-FL's accuracy under communication errors approached ideal centralized federated learning (C-FL).

    Conclusions:

    • R&A D-FL offers a more efficient and robust approach to decentralized federated learning.
    • The method shows significant synergy between D-FL and networking protocols.
    • R&A D-FL effectively mitigates communication errors, enhancing training accuracy in complex network environments.