Abstract
The rapid development of machine learning and wireless communication is creating a new paradigm for future networks, namely edge-intelligent networks. Specifically, data generated by terminal devices is processed via machine learning at the edge of wireless networks, but not at the cloud. Owing to the growing concern for privacy information sharing, federated learning, as a new branch of machine learning, is appealing in edge-intelligent networks. For federated learning, the wireless transmission capabilities under practical conditions, e.g., imperfect channel state information (CSI), have a great impact on the accuracy of global aggregation of local model updates. Therefore, it is very important to enhance the robustness of communication for federated learning. In order to realize robust communication in the presence of channel uncertainty, we propose a robust federated learning algorithm for edge-intelligent networks, including device selection, transmit power allocation, and receive beamforming. Simulation results validate the robustness and effectiveness of the proposed robust federated learning algorithm in edge-intelligent networks.
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Acknowledgements
This work was supported by National Key R&D Program of China (Grant No. 2018YFB1801104), National Natural Science Foundation of China (Grant No. 61871344), and Zhejiang Provincial Natural Science Foundation of China (Grant No. LR20F010002).
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Gao, Z., Chen, X. & Shao, X. Robust federated learning for edge-intelligent networks. Sci. China Inf. Sci. 65, 132306 (2022). https://doi.org/10.1007/s11432-020-3251-9
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DOI: https://doi.org/10.1007/s11432-020-3251-9