Over-the-air federated learning (OTA-FL) provides bandwidth-efficient learning by leveraging the inherent superposition property of wireless channels. Personalized federated learning balances performance for users with diverse datasets, addressing real-life data heterogeneity. We propose the first personalized OTA-FL scheme through multi-task learning, assisted by personal reconfigurable intelligent surfaces (RIS) for each user. We take a cross-layer approach that optimizes communication and computation resources for global and personalized tasks in time-varying channels with imperfect channel state information, using multi-task learning for non-i.i.d data. Our PROAR-PFed algorithm adaptively designs power, local iterations, and RIS configurations. We present convergence analysis for non-convex objectives and demonstrate that PROAR-PFed outperforms state-of-the-art on the Fashion-MNIST dataset.

翻译：空中联邦学习（OTA-FL）通过利用无线信道的固有叠加特性，提供带宽高效的学习方式。个性化联邦学习在用户数据集异构的现实场景中平衡性能，以应对数据异质性。我们提出首个基于多任务学习的个性化空中联邦学习方案，该方案为每个用户配备个性化的可重构智能表面（RIS）作为辅助。我们采用跨层方法，在时变信道和不完美信道状态信息条件下，为全局任务和个性化任务优化通信与计算资源，并利用多任务学习处理非独立同分布数据。提出的PROAR-PFed算法自适应地设计功率、本地迭代次数和RIS配置。我们针对非凸目标函数进行收敛性分析，并证明PROAR-PFed在Fashion-MNIST数据集上优于现有最优方法。