Federated learning (FL) is capable of analyzing tremendous data from smart edge devices in Industrial Internet of Things (IIoTs), empowering numerous industrial applications. However, the increasing privacy concerns and deployment costs of IIoT environment have been posing new challenges for FL. This article proposes PCFed, a novel privacy-enhanced and communication-efficient FL framework to provide higher model accuracy with rigorous privacy guarantees and great communication efficiency. In particular, we develop a sampling-based intermittent communication strategy via a PID (proportional, integral, and derivative) controller on the cloud server to adaptively reduce the communication frequency. In addition, we design a budget allocation mechanism to balance the tradeoff between model accuracy and privacy loss. Then, we develop PCFed+, an enhanced variant for PCFed, with further consideration of infinite data streams on edge servers. Extensive experiments demonstrate that both PCFed and PCFed+ can significantly outperform existing schemes, in terms of communication efficiency, privacy protection, and model accuracy.

中文翻译：

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PCFed：工业物联网的隐私增强和通信高效的联邦学习

联邦学习 (FL) 能够分析来自工业物联网 (IIoT) 中智能边缘设备的大量数据，为众多工业应用提供支持。然而，越来越多的隐私问题和 IIoT 环境的部署成本给 FL 带来了新的挑战。本文提出了 PCFed，一种新颖的隐私增强和通信高效的 FL 框架，以提供更高的模型精度、严格的隐私保证和出色的通信效率。特别是，我们通过云服务器上的 PID（比例、积分和微分）控制器开发了一种基于采样的间歇通信策略，以自适应地降低通信频率。此外，我们设计了一种预算分配机制来平衡模型准确性和隐私损失之间的权衡。然后，我们开发PCFed+，PCFed 的增强变体，进一步考虑了边缘服务器上的无限数据流。大量实验表明，PCFed 和 PCFed+ 在通信效率、隐私保护和模型准确性方面都可以显着优于现有方案。