Suchergebnisse - "algorithm parameter configuration"

Knowledge- and Model-Driven Deep Reinforcement Learning for Efficient Federated Edge Learning: Single- and Multi-Agent Frameworks von Li, Yangchen, Zhao, Lingzhi, Wang, Tianle, Ding, Lianghui, Yang, Feng

“… a rigorous convergence analysis. We formulate a joint optimization problem for FL worker selection and algorithm parameter configuration to minimize the final test loss subject to time and energy constraints …”
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