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Federated Learning (FL) is a prominent method in machine learning, that ensures privacy by enabling distributed devices to collaboratively learn a shared model without exchanging local data. This paper provides a comparative analysis of various FL algorithms implemented on the Smart Python Agent Development Environment (SPADE) framework. We focus on evaluating the performance, scalability, and resilience of these algorithms across different network setups and data distribution scenarios. Our results highlight the differential impacts of decentralized versus centralized approaches, particularly under non-IID data conditions, common in real-world applications. By leveraging SPADE agents and consensus algorithms, this study not only tests algorithmic efficiency and system robustness but also explores advanced strategies like asynchronous updates and coalition-based learning, which show promise in enhancing model accuracy and reducing communication overhead.

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Comparative Study of Federated Learning Algorithms Based on SPADE Agents

  • 会议论文

  • 发表日期：

    2025年

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  • Francisco Enguix,
  • Saúl Cerdá Peris,
  • J. A. Rincon,
  • C. Carrascosa

摘要

Federated Learning (FL) is a prominent method in machine learning, that ensures privacy by enabling distributed devices to collaboratively learn a shared model without exchanging local data. This paper provides a comparative analysis of various FL algorithms implemented on the Smart Python Agent Development Environment (SPADE) framework. We focus on evaluating the performance, scalability, and resilience of these algorithms across different network setups and data distribution scenarios. Our results highlight the differential impacts of decentralized versus centralized approaches, particularly under non-IID data conditions, common in real-world applications. By leveraging SPADE agents and consensus algorithms, this study not only tests algorithmic efficiency and system robustness but also explores advanced strategies like asynchronous updates and coalition-based learning, which show promise in enhancing model accuracy and reducing communication overhead.