Hyper-parameter settings.

<div><p><i>Federated Learning</i> (FL), as a distributed computing framework for training machine learning (ML) models, has garnered significant attention for its superior privacy protection. In typical FL, a subset of client models is randomly selected for aggregation in eac...

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ग्रंथसूची विवरण
मुख्य लेखक:	Pan Wang (198097) (author)
अन्य लेखक:	Zhengyi Zhong (22057223) (author), Ji Wang (137458) (author)
प्रकाशित:	2025
विषय:	Biotechnology Cancer Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified training machine learning superior privacy protection show superior performance garnered significant attention federated learning </ encoding client ids evolutionary algorithm </ diverse data distributions distributed computing framework different data distributions optimizing client selection base models </ identify base models client models base models random selection evolutionary operations identically distributed world scenarios typical fl todaynews datasets sufficiently demonstrate slower convergence randomly selected proposed method performs well method addresses lower accuracy low efficiency iid ). global model fl frameworks fl ), conduct experiments
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Hyper-parameter settings.

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