Federated learning collaboratively trains a neural network on privately owned data held by several participating clients. The gradient descent algorithm, a well-known and popular iterative optimization procedure, is run to train the neural network. Every client uses its local data to compute partial gradients and sends it to the federator which aggregates the results. Privacy of the clients' data is a major concern. In fact, observing the partial gradients can be enough to reveal the clients' data. Private aggregation schemes have been investigated to tackle the privacy problem in federated learning where all the users are connected to each other and to the federator. In this paper, we consider a wireless system architecture where clients are only connected to the federator via base stations. We derive fundamental limits on the communication cost when information-theoretic privacy is required, and introduce and analyze a private aggregation scheme tailored for this setting.

翻译：联邦学习通过多个参与客户端持有的私有数据协同训练神经网络。作为广为人知且广泛使用的迭代优化流程，梯度下降算法被用于神经网络训练。每个客户端利用本地数据计算部分梯度，并将结果发送至执行聚合操作的联邦器。客户端数据的隐私性是一个关键问题。事实上，观察部分梯度可能足以泄露客户端数据。已有研究探讨了适用于所有用户相互连接且连接联邦器的联邦学习场景中的私有聚合方案。本文考虑一种无线系统架构，其中客户端仅通过基站与联邦器连接。我们推导了在要求信息论隐私前提下的通信成本基本极限，并针对该场景提出并分析了一种定制化私有聚合方案。