Aug 7, 2024 · We study the joint edge aggregation and association problem to achieve the cost-efficient FL performance, where the model aggregation over multiple cells just ...
Oct 17, 2024 · At its core, FL enables many user equipments (UEs) to collaboratively train an ML model with the help of a central parameter server without ...
Thus motivated, we study the joint edge aggregation and association problem to achieve the cost-efficient FL performance, where the model aggregation over ...
Aug 29, 2024 · Federated learning is an effective solution for edge training, but the limited bandwidth and insufficient computing resources of edge devices ...
Sep 12, 2021 · In this paper, we analyze how to design adaptive FL in mobile edge networks that optimally chooses these essential control variables to minimize the total cost ...
Missing: Intelligence Multi-
Abstract—Federated learning (FL) has been proposed as a promising distributed learning paradigm to realize edge artificial intelligence (AI) without ...
Federated Learning (FL) allows multiple heterogeneous clients to cooperatively train models without disclosing private data. However, selfish clients may be ...
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Oct 22, 2024 · In this paper, we analyze how to design adaptive FL in mobile edge networks that optimally chooses these essential control variables to minimize ...
Sep 12, 2021 · Abstract—Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to.
4 days ago · The author in40 presents an energy-efficient task-scheduling algorithm for mobile edge computing based on traffic mapping.