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.

我们提出了第一个个性化的OTA-FL方案，通过多任务学习以及用户个人可重构智能表面（RIS）的协助，平衡了具有不同数据集的用户的性能，解决了现实生活中数据异质性问题。我们的PROAR-PFed算法通过优化通信和计算资源，针对时变信道中的全局和个性化任务，在非独立同分布数据的情况下，自适应地设计功率、本地迭代和RIS配置。通过收敛性分析，我们证明PROAR-PFed在Fashion-MNIST数据集上胜过现有的最先进方法。

个性化面向个性化重构智能表面的无线联邦学习