平衡可重构智能表面辅助空中联邦学习的准确性和完整性
Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning
摘要：
空中联邦学习（AirFL）允许设备并行训练学习模型，并使用空中计算来同步它们的本地模型。AirFL的完整性是脆弱的，由于模糊的本地模型聚集在空中。本文提出了一种新的框架来平衡AirFL的准确性和完整性，其中多天线设备和基站（BS）与可重构智能表面（RIS）联合优化。主要贡献包括一个新的和非平凡的问题，共同考虑模型的准确性和完整性的AirFL，和一个新的框架，将问题转化为易于处理的子问题。在理想信道状态信息（CSI）下，通过交替优化设备的发射波束形成器、基站的接收波束形成器和RIS配置，新框架最大限度地减小了聚合模型的失真，并保持了局部模型的可恢复性。在不完美CSI下，新框架提供了波束形成器和RIS配置的鲁棒设计，以对抗不可忽略的信道估计误差。实验证明，该新框架可以实现与理想FL相当的精度，同时在完美CSI下保持局部模型可恢复性，并且在不完美CSI下当接收天线数量较少或中等时提高精度。
英文摘要：
Over-the-air federated learning (AirFL) allows devices to train a learning model in parallel and synchronize their local models using over-the-air computation. The integrity of AirFL is vulnerable due to the obscurity of the local models aggregated over-the-air. This paper presents a novel framework to balance the accuracy and integrity of AirFL, where multi-antenna devices and base station (BS) are jointly optimized with a reconfigurable intelligent surface (RIS). The key contributions include a new and non-trivial problem jointly considering the model accuracy and integrity of AirFL, and a new framework that transforms the problem into tractable subproblems. Under perfect channel state information (CSI), the new framework minimizes the aggregated model's distortion and retains the local models' recoverability by optimizing the transmit beamformers of the devices, the receive beamformers of the BS, and the RIS configuration in an alternating manner. Under imperfect CSI, the new framework delivers a robust design of the beamformers and RIS configuration to combat non-negligible channel estimation errors. As corroborated experimentally, the novel framework can achieve comparable accuracy to the ideal FL while preserving local model recoverability under perfect CSI, and improve the accuracy when the number of receive antennas is small or moderate under imperfect CSI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
