Integrating Explainable AI with Federated Learning for Next-Generation IoT: A comprehensive review and prospective insights

Abstract

The emergence of the Internet of Things (IoT) signifies a transformative wave of innovation, establishing a network of devices designed to enrich everyday experiences. Developing intelligent and secure IoT applications without compromising user privacy and the transparency of model decisions causes a significant challenge. Federated Learning (FL) serves as a innovative solution, encouraging collaborative learning across a wide range of devices and ensures the protection of user data and builds trust in the process. However, challenges remain, including data variability, potential security vulnerabilities within FL, and the necessity for transparency in decentralized models. Moreover, the lack of clarity associated with traditional AI models raises issues regarding transparency, trust and fairness in IoT applications. The survey examines the integration of Explainable AI (XAI) and FL within the Next Generation IoT framework. It provides a thorough analysis of how XAI techniques can elucidate the mechanisms of FL models, addressing challenges such as communication overhead, data heterogeneity and privacy-preserving explanation methods. The survey brings attention to the benefits of FL, including secure data sharing, effective modeling of heterogeneous data and improved communication and interoperability. Additionally, it presents mathematical formulations of the challenges in FL and discusses potential solutions aimed at enhancing the resilience and scalability of IoT implementations. Eventually, convergence of XAI and FL enhances interpretability and promotes the development of trustworthy and transparent AI systems, establishing a strong foundation for impactful applications in the ever evolving Next-Generation IoT landscape.