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Device Scheduling with Fast Convergence for Wireless Federated Learning
Owing to the increasing need for massive data analysis and model trainin...
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Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks
Federated learning (FL) is a machine learning model that preserves data ...
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Energy Efficient Federated Learning Over Wireless Communication Networks
In this paper, the problem of energy efficient transmission and computat...
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Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
In federated learning (FL), devices contribute to the global training by...
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Scheduling Policies for Federated Learning in Wireless Networks
Motivated by the increasing computational capacity of wireless user equi...
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Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective
This paper studies federated learning (FL) in a classic wireless network...
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Key technologies to accelerate the ICT Green evolution -- An operator's point of view
The exponential growth in networks' traffic accompanied by the multiplic...
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Sliding Differential Evolution Scheduling for Federated Learning in Bandwidth-Limited Networks
Federated learning (FL) in a bandwidth-limited network with energy-limited user equipments (UEs) is under-explored. In this paper, to jointly save energy consumed by the battery-limited UEs and accelerate the convergence of the global model in FL for the bandwidth-limited network, we propose the sliding differential evolution-based scheduling (SDES) policy. To this end, we first formulate an optimization that aims to minimize a weighted sum of energy consumption and model training convergence. Then, we apply the SDES with parallel differential evolution (DE) operations in several small-scale windows, to address the above proposed problem effectively. Compared with existing scheduling policies, the proposed SDES performs well in reducing energy consumption and the model convergence with lower computational complexity.
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