A Review on Federated Learning Architectures for Privacy-Preserving AI: Lightweight and Secure Cloud–Edge–End Collaboration
Shanhao Zhan, Lianfen Huang, Gaoyu Luo, Shaolong Zheng, Zhibin Gao, Han‐Chieh Chao
Abstract
Federated learning (FL) has emerged as a promising paradigm for enabling collaborative training of machine learning models while preserving data privacy. However, the massive heterogeneity of data and devices, communication constraints, and security threats pose significant challenges to its practical implementation. This paper provides a system review of the state-of-the-art techniques and future research directions in FL, with a focus on addressing these challenges in resource-constrained environments by a cloud–edge–end collaboration FL architecture. We first introduce the foundations of cloud–edge–end collaboration and FL. We then discuss the key technical challenges. Next, we delve into the pillars of trustworthy AI in the federated context, covering robustness, fairness, and explainability. We propose a dimension reconstruction of trusted AI and analyze the foundations of each trustworthiness pillar. Furthermore, we present a lightweight FL framework for resource-constrained edge–end devices, analyzing the core contradictions and proposing optimization paradigms. Finally, we highlight advanced topics and future research directions to provide valuable insights into the field.