Sliding Differential Evolution Scheduling for Federated Learning in
Bandwidth-Limited Networks
- URL: http://arxiv.org/abs/2010.08991v1
- Date: Sun, 18 Oct 2020 14:08:24 GMT
- Title: Sliding Differential Evolution Scheduling for Federated Learning in
Bandwidth-Limited Networks
- Authors: Yifan Luo, Jindan Xu, Wei Xu, Kezhi Wang
- Abstract summary: Federated learning (FL) in a bandwidth-limited network with energy-limited user equipments (UEs) is under-explored.
We propose the sliding differential evolution-based scheduling (SDES) policy 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.
- Score: 23.361422744588978
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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