In a multi-agent system, agents can cooperatively learn a model from data by exchanging their estimated model parameters, without the need to exchange the locally available data used by the agents. This strategy, often called federated learning, is mainly employed for two reasons: (i) improving resource-efficiency by avoiding to share potentially large datasets and (ii) guaranteeing privacy of local agents' data. Efficiency can be further increased by adopting a beyond-5G communication strategy that goes under the name of Over-the-Air Computation. This strategy exploits the interference property of the wireless channel. Standard communication schemes prevent interference by enabling transmissions of signals from different agents at distinct time or frequency slots, which is not required with Over-the-Air Computation, thus saving resources. In this case, the received signal is a weighted sum of transmitted signals, with unknown weights (fading channel coefficients). State of the art papers in the field aim at reconstructing those unknown coefficients. In contrast, the approach presented here does not require reconstructing channel coefficients by complex encoding-decoding schemes. This improves both efficiency and privacy.

翻译：在多智能体系统中，智能体可通过交换各自估计的模型参数来协作从数据中学习模型，而无需交换本地可用数据。这种策略通常被称为联邦学习，其主要基于两个原因：一是通过避免共享潜在的大规模数据集来提高资源效率，二是保障本地智能体数据的隐私性。采用名为“空中计算”的超越5G通信策略可进一步提升效率。该策略利用无线信道的干扰特性。标准通信方案通过在不同时间或频率时隙传输来自不同智能体的信号来避免干扰，而空中计算无需如此，从而节省资源。在此情况下，接收信号是各传输信号的加权和，但权重（即衰落信道系数）未知。现有领域内前沿论文旨在重构这些未知系数。与之相反，本文提出的方法无需通过复杂的编解码方案重构信道系数，从而兼顾了效率与隐私性的提升。