TY - GEN
T1 - Federated Learning in Multi-antenna Wireless Networks
AU - Song, Zhendong
AU - Sun, Hongguang
AU - Yang, Howard H.
AU - Wang, Xijun
AU - Quek, Tony Q.S.
N1 - Funding Information:
Z111021901, in part by the National Natural Science Foundation of China under Grant 61701377, in part by the Zhe-jiang University/University of Illinois at Urbana-Champaign Institute starting fund, in part by the GuangDong Basic and Applied Basic Research Foundation under grant 2021A1515012631, in part by the Fundamental Research Funds for the Central Universities under grant 19lgpy79, in part by the Research Fund of the Key Laboratory of Wireless Sensor Network & Communication (Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences) under grant 20190912.
Funding Information:
This work was supported in part by the Talents Special Foundation of Northwest A&F University under Grant
Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - In this work, we propose an analytical model to study the convergence performance of federated learning (FL) in a multi-antenna wireless network by comparing three different scheduling policies, i.e., the round robin (RR), random scheduling (RS), and proportional fair (PF). We derive tractable expressions for the convergence rates of FL by taking into account the scheduling policy, antenna number, channel fading, and intercell interference. Our results show that PF achieves the best convergence performance under the high signal-to-interference-plus-noise ratio (SINR) threshold, while the three scheduling policies achieve very similar convergence rates under extremely low SINR threshold. Given the number of antennas per base station (BS), we observe an optimal number of scheduled user equipments (UEs) per BS that maximizes the convergence rate of FL under high SINR threshold because of the trade-off between serving more UEs and achieving higher successful transmission probability.
AB - In this work, we propose an analytical model to study the convergence performance of federated learning (FL) in a multi-antenna wireless network by comparing three different scheduling policies, i.e., the round robin (RR), random scheduling (RS), and proportional fair (PF). We derive tractable expressions for the convergence rates of FL by taking into account the scheduling policy, antenna number, channel fading, and intercell interference. Our results show that PF achieves the best convergence performance under the high signal-to-interference-plus-noise ratio (SINR) threshold, while the three scheduling policies achieve very similar convergence rates under extremely low SINR threshold. Given the number of antennas per base station (BS), we observe an optimal number of scheduled user equipments (UEs) per BS that maximizes the convergence rate of FL under high SINR threshold because of the trade-off between serving more UEs and achieving higher successful transmission probability.
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U2 - 10.1109/ICCWorkshops50388.2021.9473537
DO - 10.1109/ICCWorkshops50388.2021.9473537
M3 - Conference contribution
AN - SCOPUS:85112822270
T3 - 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings
BT - 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021
Y2 - 14 June 2021 through 23 June 2021
ER -