Semi-Federated Learning

• URL: http://arxiv.org/abs/2003.12795v1
• Date: Sat, 28 Mar 2020 14:08:59 GMT
• Title: Semi-Federated Learning
• Authors: Zhikun Chen, Daofeng Li, Ming Zhao, Sihai Zhang, Jinkang Zhu
• Abstract summary: Federated learning (FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. We propose the Semi-Federated Learning (Semi-FL) which differs from the FL in two aspects, local clients clustering and in-cluster training. The proposed Semi-FL can be easily applied to future mobile communication networks and require less up-link transmission bandwidth.
• Score: 4.897245729430157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. However, the practicality, communication expense and non-independent and identical distribution (Non-IID) data challenges in FL still need to be concerned. In this work, we propose the Semi-Federated Learning (Semi-FL) which differs from the FL in two aspects, local clients clustering and in-cluster training. A sequential training manner is designed for our in-cluster training in this paper which enables the neighboring clients to share their learning models. The proposed Semi-FL can be easily applied to future mobile communication networks and require less up-link transmission bandwidth. Numerical experiments validate the feasibility, learning performance and the robustness to Non-IID data of the proposed Semi-FL. The Semi-FL extends the existing potentials of FL.

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