Internet-of-things (IoT) is a revolutionary paragon that brings automation and easiness to human lives and improves their experience. Smart Homes, Healthcare, and Agriculture are some of their amazing use cases. These IoT applications often employ Machine Learning (ML) techniques to strengthen their functionality. ML can be used to analyze sensor data for various, including optimizing energy usage in smart homes, predicting maintenance needs in industrial equipment, personalized user experiences in wearable devices, and detecting anomalies for security monitoring. However, implementing centralized ML techniques is not viable because of the high cost of computing power and privacy issues since so much data is stored over a cloud server. To safeguard data privacy, Federated Learning (FL) has become a new paragon for centralized ML methods where FL,an ML variation sends a model to the user devices without the need to give private data to the third-party or central server, it is one of the promising solutions to address data leakage concerns. By saving raw data to the client itself and transferring only model updates or parameters to the central server, FL helps to reduce privacy leakage. However, it is still not attack-resistant. Blockchain offers a solution to protect FL-enabled IoT networks using smart contracts and consensus mechanisms. This manuscript reviews IoT applications and challenges, discusses FL techniques that can be used to train IoT networks while ensuring privacy, and analyzes existing work. To ensure the security and privacy of IoT applications, an integrated Blockchain-powered FL-based framework was introduced and studies existing research were done using these three powerful paradigms. Finally, the research challenges faced by the integrated platform are explored for future scope, along with the potential applications of IoT in conjunction with other cutting-edge technologies.

中文翻译：

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释放区块链-联合学习融合对 IoT 安全性的前景：全面回顾


物联网 （IoT） 是一个革命性的典范，它为人类生活带来了自动化和轻松性，并改善了他们的体验。智能家居、医疗保健和农业是他们一些令人惊叹的用例。这些 IoT 应用程序通常采用机器学习 （ML） 技术来增强其功能。ML 可用于分析各种传感器数据，包括优化智能家居的能源使用、预测工业设备的维护需求、可穿戴设备中的个性化用户体验以及检测安全监控的异常情况。但是，由于计算能力成本高昂，而且由于大量数据存储在云服务器上，因此实施集中式 ML 技术是不可行的。为了保护数据隐私，联邦学习 （FL） 已成为集中式 ML 方法的新典范，其中 FL 是一种 ML 变体，可将模型发送到用户设备，而无需将私人数据提供给第三方或中央服务器，它是解决数据泄漏问题的有前途的解决方案之一。通过将原始数据保存到客户端本身，并且仅将模型更新或参数传输到中央服务器，FL 有助于减少隐私泄露。但是，它仍然不耐攻击。区块链提供了一种解决方案，可以使用智能合约和共识机制保护支持 FL 的 IoT 网络。本手稿回顾了 IoT 应用和挑战，讨论了可用于训练 IoT 网络同时确保隐私的 FL 技术，并分析了现有工作。为了确保物联网应用程序的安全性和隐私性，引入了一个集成的基于区块链驱动的 FL 框架，并使用这三种强大的范式完成了现有研究。 最后，探讨了集成平台在未来面临的研究挑战，以及物联网与其他尖端技术相结合的潜在应用。