Secure Federated Learning for Cloud-Fog Automation: Vulnerabilities, Challenges, Solutions, and Future Directions

Zhuangzhuang Zhang, Libing Wu, Jiong Jin, Enshu Wang, Bingyi Liu, Qing-Long Han

Abstract: With the intelligence and automation of industrial Internet of Things, a new collaborative Cloud-Fog Automation paradigm has emerged. The emergence of federated learning (FL) has further enhanced the capabilities of Cloud-Fog Automation, making it possible to develop more secure and versatile collaborative industrial models. However, FL faces various security risks. More importantly, the security risks faced by FL when applied in Cloud-Fog Automation, along with corresponding security measures, have not yet been explored. To address this issue, we make an initial attempt to analyze the security of FL within the context of Cloud-Fog Automation, with the aim of facilitating the design of a more secure FL framework for this paradigm. Specifically, we first analyze the security risks that may be encountered at different phases, then analyze the challenges that need to be faced to resolve these risks. Subsequently, we conduct a systematic review of the state-of-the-art security solutions, and finally summarize the future research directions.

External IDs:dblp:journals/tii/ZhangWJWLH25