Federated learning (FL) is a privacy-preserving machine learning setting that enables many devices to jointly train a shared global model without the need to reveal their data to a central server. However, FL involves a frequent exchange of the parameters between all the clients and the server that coordinates the training. This introduces extensive communication overhead, which can be a major bottleneck in FL with limited communication links. In this paper, we consider training the binary neural networks (BNNs) in the FL setting instead of the typical real-valued neural networks to fulfill the stringent delay and efficiency requirement in wireless edge networks. We introduce a novel FL framework of training BNNs, where the clients only upload the binary parameters to the server. We also propose a novel parameter updating scheme based on the Maximum Likelihood (ML) estimation that preserves the performance of the BNN even without the availability of aggregated real-valued auxiliary parameters that are usually needed during the training of the BNN. Moreover, for the first time in the literature, we theoretically derive the conditions under which the training of BNN is converging. Numerical results show that the proposed FL framework significantly reduces the communication cost compared to the conventional neural networks with typical real-valued parameters, and the performance loss incurred by the binarization can be further compensated by a hybrid method.

中文翻译：

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使用二元神经网络进行高效通信的联邦学习

联合学习 (FL) 是一种保护隐私的机器学习设置，它使许多设备能够共同训练共享的全局模型，而无需将其数据透露给中央服务器。然而，FL 涉及在所有客户端和协调训练的服务器之间频繁交换参数。这引入了大量的通信开销，这可能是 FL 中通信链接有限的主要瓶颈。在本文中，我们考虑在 FL 设置中训练二元神经网络 (BNN)，而不是典型的实值神经网络，以满足无线边缘网络中严格的延迟和效率要求。我们引入了一种新的训练 BNN 的 FL 框架，其中客户端仅将二进制参数上传到服务器。我们还提出了一种基于最大似然 (ML) 估计的新参数更新方案，即使没有在 BNN 训练期间通常需要的聚合实值辅助参数的可用性，该方案也能保持 BNN 的性能。此外，在文献中，我们第一次从理论上推导出 BNN 的训练收敛的条件。数值结果表明，与具有典型实值参数的传统神经网络相比，所提出的 FL 框架显着降低了通信成本，并且二值化引起的性能损失可以通过混合方法进一步补偿。