FedDCT: A Dynamic Cross-Tier Federated Learning Scheme in Wireless
Communication Networks
- URL: http://arxiv.org/abs/2307.04420v1
- Date: Mon, 10 Jul 2023 08:54:07 GMT
- Title: FedDCT: A Dynamic Cross-Tier Federated Learning Scheme in Wireless
Communication Networks
- Authors: Peng Liu, Youquan Xian, Chuanjian Yao, Xiaoyun Gan, Lianghaojie Zhou,
Jianyong Jiang, Dongcheng Li
- Abstract summary: Federated Learning (FL) enables the training of a global model among clients without exposing local data.
We propose a novel dynamic cross-tier FL scheme, named FedDCT, to increase training accuracy and performance in wireless communication networks.
- Score: 1.973745731206255
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the rapid proliferation of Internet of Things (IoT) devices and the
growing concern for data privacy among the public, Federated Learning (FL) has
gained significant attention as a privacy-preserving machine learning paradigm.
FL enables the training of a global model among clients without exposing local
data. However, when a federated learning system runs on wireless communication
networks, limited wireless resources, heterogeneity of clients, and network
transmission failures affect its performance and accuracy. In this study, we
propose a novel dynamic cross-tier FL scheme, named FedDCT to increase training
accuracy and performance in wireless communication networks. We utilize a
tiering algorithm that dynamically divides clients into different tiers
according to specific indicators and assigns specific timeout thresholds to
each tier to reduce the training time required. To improve the accuracy of the
model without increasing the training time, we introduce a cross-tier client
selection algorithm that can effectively select the tiers and participants.
Simulation experiments show that our scheme can make the model converge faster
and achieve a higher accuracy in wireless communication networks.
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