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.

中文翻译：

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将可解释的 AI 与联邦学习集成以实现下一代 IoT：全面回顾和前瞻性见解


物联网 （IoT） 的出现标志着变革性的创新浪潮，建立了一个旨在丰富日常体验的设备网络。在不损害用户隐私和模型决策透明度的情况下开发智能且安全的 IoT 应用程序是一项重大挑战。联邦学习 （FL） 是一种创新的解决方案，鼓励在各种设备上进行协作学习，并确保保护用户数据并在此过程中建立信任。然而，挑战仍然存在，包括数据可变性、联邦学习中的潜在安全漏洞以及去中心化模型中透明度的必要性。此外，与传统 AI 模型相关的缺乏明确性引发了 IoT 应用程序的透明度、信任和公平性问题。该调查研究了可解释 AI （XAI） 和 FL 在下一代物联网框架中的集成。它全面分析了 XAI 技术如何阐明 FL 模型的机制，解决了通信开销、数据异构性和隐私保护解释方法等挑战。该调查引起了人们对 FL 优势的关注，包括安全的数据共享、异构数据的有效建模以及改进的通信和互操作性。此外，它还提出了 FL 中挑战的数学公式，并讨论了旨在增强 IoT 实现的弹性和可扩展性的潜在解决方案。最终，XAI 和 FL 的融合增强了可解释性，并促进了值得信赖和透明的 AI 系统的发展，为在不断发展的下一代物联网环境中有影响力的应用程序奠定了坚实的基础。