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This paper presents a novel approach for calculating average weights in Federated Learning. This technique integrates data-related measures, such as Data Quality, Data Freshness, and Data Importance. These changes provide a more accurate and pertinent averaging procedure, resulting in improved models and more relevant model output. This technology is tested and validated within an agricultural scenario, that employs a Low-Communication paradigm. Additionally, the metadata serves as a backup alerting mechanism to detect any issues inside the system.

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Aggregation Strategy for Federated Machine Learning Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Rudolf Erdei,
  • Daniela Delinschi,
  • Iulia Bărăian,
  • Oliviu Matei

摘要

This paper presents a novel approach for calculating average weights in Federated Learning. This technique integrates data-related measures, such as Data Quality, Data Freshness, and Data Importance. These changes provide a more accurate and pertinent averaging procedure, resulting in improved models and more relevant model output. This technology is tested and validated within an agricultural scenario, that employs a Low-Communication paradigm. Additionally, the metadata serves as a backup alerting mechanism to detect any issues inside the system.