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.
翻译:空外联手学习(AirFL)使设备能够同时和同步地利用空外计算来训练学习模式,使当地模型同步。AirFL的完整由于地方模型的模糊性而变得脆弱。本文提供了一个新的框架,以平衡空气Fal(多亚硝干装置和基地站(BS)的准确性和完整性,在空气FL(AirFL)的精确性和完整性与可重新配置的智能表面(RIS)共同优化。关键贡献包括一个新的和非三联问题,考虑到AirFL的模型准确性和完整性,以及将问题转化为可移植子问题的新框架。在完美的频道状态信息(CSI)下,新的框架最大限度地减少了综合模型的扭曲性,并通过优化设备的传输光谱和光基站(BS)的光亮度来保持当地模型的可恢复性。在不完善的CSI(C)下,新的框架提供了可靠的光谱和RIS配置设计,以在不精确性CSI(C)下,在可比较的精确度下,在精确性CI(C)下,可以实现可复制的精确性框架。