Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing

Federated Learning (FL) has recently attracted considerable attention in internet of things, due to its capability of enabling mobile clients to collaboratively learn a global prediction model without sharing their privacy-sensitive data to the server. Despite its great potential, a main challenge o...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.24462-24474
Main Authors: Zhang, Wenyu, Wang, Xiumin, Zhou, Pan, Wu, Weiwei, Zhang, Xinglin
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
client selection
Clients
Computational modeling
Computer simulation
Data models
Degradation
Distributed databases
Edge computing
Federated learning
Internet of Things
Machine learning
Mobile computing
mobile edge computing
Model accuracy
Prediction models
Servers
Training
Weight
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Summary:Federated Learning (FL) has recently attracted considerable attention in internet of things, due to its capability of enabling mobile clients to collaboratively learn a global prediction model without sharing their privacy-sensitive data to the server. Despite its great potential, a main challenge of FL is that the training data are usually non-Independent, Identically Distributed (non-IID) on the clients, which may bring the biases in the model training and cause possible accuracy degradation. To address this issue, this paper aims to propose a novel FL algorithm to alleviate the accuracy degradation caused by non-IID data at clients. Firstly, we observe that the clients with different degrees of non-IID data present heterogeneous weight divergence with the clients owning IID data. Inspired by this, we utilize weight divergence to recognize the non-IID degrees of clients. Then, we propose an efficient FL algorithm, named CSFedAvg , in which the clients with lower degree of non-IID data will be chosen to train the models with higher frequency. Finally, we conduct simulations using publicly-available datasets to train deep neural networks. Simulation results show that the proposed FL algorithm improves the training performance compared with existing FL protocol.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3056919