RIS-Assisted Federated Learning in Multi-Cell Wireless Networks  

作　　者：WANG Yiji WEN Dingzhu MAO Yijie SHI Yuanming 

机构地区：[1]ShanghaiTech University,Shanghai 201210,China

出　　处：《ZTE Communications》2023年第1期25-37,共13页中兴通讯技术（英文版）

摘　　要：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.

关 键 词：federated learning(FL) reconfigurable intelligent surface(RIS) over-the-air computation(AirComp) multi-cell networks 

分 类 号：TN92[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]