Table of Content

    22 March 2023, Volume 21 Issue 1
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    The whole issue of ZTE Communications March 2023, Vol. 21 No. 1
    2023, 21(1):  0. 
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    Special Topic
    Special Topic on Federated Learning over Wireless Networks
    CUI Shuguang, YIN Changchuan, ZHU Guangxu
    2023, 21(1):  1-2.  doi:10.12142/ZTECOM.202301001
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    Adaptive Retransmission Design for Wireless Federated Edge Learning
    XU Xinyi, LIU Shengli, YU Guanding
    2023, 21(1):  3-14.  doi:10.12142/ZTECOM.202301002
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    As a popular distributed machine learning framework, wireless federated edge learning (FEEL) can keep original data local, while uploading model training updates to protect privacy and prevent data silos. However, since wireless channels are usually unreliable, there is no guarantee that the model updates uploaded by local devices are correct, thus greatly degrading the performance of the wireless FEEL. Conventional retransmission schemes designed for wireless systems generally aim to maximize the system throughput or minimize the packet error rate, which is not suitable for the FEEL system. A novel retransmission scheme is proposed for the FEEL system to make a tradeoff between model training accuracy and retransmission latency. In the proposed scheme, a retransmission device selection criterion is first designed based on the channel condition, the number of local data, and the importance of model updates. In addition, we design the air interface signaling under this retransmission scheme to facilitate the implementation of the proposed scheme in practical scenarios. Finally, the effectiveness of the proposed retransmission scheme is validated through simulation experiments.

    Reliable and Privacy-Preserving Federated Learning with Anomalous Users
    ZHANG Weiting, LIANG Haotian, XU Yuhua, ZHANG Chuan
    2023, 21(1):  15-24.  doi:10.12142/ZTECOM.202301003
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    Recently, various privacy-preserving schemes have been proposed to resolve privacy issues in federated learning (FL). However, most of them ignore the fact that anomalous users holding low-quality data may reduce the accuracy of trained models. Although some existing works manage to solve this problem, they either lack privacy protection for users’ sensitive information or introduce a two-cloud model that is difficult to find in reality. A reliable and privacy-preserving FL scheme named reliable and privacy-preserving federated learning (RPPFL) based on a single-cloud model is proposed. Specifically, inspired by the truth discovery technique, we design an approach to identify the user’s reliability and thereby decrease the impact of anomalous users. In addition, an additively homomorphic cryptosystem is utilized to provide comprehensive privacy preservation (user’s local gradient privacy and reliability privacy). We give rigorous theoretical analysis to show the security of RPPFL. Based on open datasets, we conduct extensive experiments to demonstrate that RPPEL compares favorably with existing works in terms of efficiency and accuracy.

    RIS-Assisted Federated Learning in Multi-Cell Wireless Networks
    WANG Yiji, WEN Dingzhu, MAO Yijie, SHI Yuanming
    2023, 21(1):  25-37.  doi:10.12142/ZTECOM.202301004
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    Over-the-air computation (AirComp) based federated learning (FL) has been a promising technique for distilling artificial intelligence (AI) at the network edge. However, the performance of AirComp-based FL is decided by the device with the lowest channel gain due to the signal alignment property. More importantly, most existing work focuses on a single-cell scenario, where inter-cell interference is ignored. To overcome these shortages, a reconfigurable intelligent surface (RIS)-assisted AirComp-based FL system is proposed for multi-cell networks, where a RIS is used for enhancing the poor user signal caused by channel fading, especially for the device at the cell edge, and reducing inter-cell interference. The convergence of FL in the proposed system is first analyzed and the optimality gap for FL is derived. To minimize the optimality gap, we formulate a joint uplink and downlink optimization problem. The formulated problem is then divided into two separable nonconvex subproblems. Following the successive convex approximation (SCA) method, we first approximate the nonconvex term to a linear form, and then alternately optimize the beamforming vector and phase-shift matrix for each cell. Simulation results demonstrate the advantages of deploying a RIS in multi-cell networks and our proposed system significantly improves the performance of FL.