Scholarlink

<p>In federated learning, most of the participating clients will have significant heterogeneity in data distribution, and this difference will lead to many challenges in the training process of the global model. By selecting high-quality clients to participate in training, the adaptability of the model to different data characteristics can be improved. We propose a dynamic weighted federal client selection method (GAFL) based on an adaptive genetic algorithm, where genes on chromosomes represent different weight combinations of all clients, and the optimal client combinations are found by dynamically adjusting the crossover probability through adaptive genetics on chromosome populations combined with a dynamic weight assignment strategy. This dual dynamic mechanism makes our method more flexible and greatly improves the performance of the model. Through experiments, the GAFL method outperforms the traditional algorithm on several medical datasets.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Dynamic weight federated client selection using adaptive genetic algorithm

  • 期刊论文

  • 发表日期：

    2025年9月15日

查看全文
  • Yangzhi Lin,
  • Xiaoli Zhao,
  • Zilong Yin

摘要

In federated learning, most of the participating clients will have significant heterogeneity in data distribution, and this difference will lead to many challenges in the training process of the global model. By selecting high-quality clients to participate in training, the adaptability of the model to different data characteristics can be improved. We propose a dynamic weighted federal client selection method (GAFL) based on an adaptive genetic algorithm, where genes on chromosomes represent different weight combinations of all clients, and the optimal client combinations are found by dynamically adjusting the crossover probability through adaptive genetics on chromosome populations combined with a dynamic weight assignment strategy. This dual dynamic mechanism makes our method more flexible and greatly improves the performance of the model. Through experiments, the GAFL method outperforms the traditional algorithm on several medical datasets.