# Machine Learning for Blockchain and IoT Systems in Smart City
###### Abstract  
In this paper, we propose a blockchain-assisted federated learning framework for secure and self-optimizing digital twins in industrial IoT. The study emphasizes the need for secure and adaptable AI models to optimize digital twins in the Industrial Internet of Things (IIoT). The study emphasized the need for robust security architecture to achieve model accuracy and trust in IIoT networks capable of enabling digital twins, virtual replicas of physical assets, toward accelerating real-time decision-making. In this study, the authors projected a new framework integrating blockchain and federated learning (FL) to achieve the security of the investigated network. AI models were deployed on edge devices in an FL setting to facilitate collaboration across industrial assets and achieve data privacy. Data management and transparency were enabled using blockchain, and explainable AI (XAI) was employed to interpret the results from the framework. [PERSON] et al. (Contribution 2) highlight the nonlinear dynamics and machine learning for robotic control systems in IoT applications. The study presents a novel approach to robotic control by integrating nonlinear dynamics with machine learning in an IoT environment. Specifically, the authors address the need for adaptable, real-time control systems capable of handling complex, nonlinear dynamic environments, leveraging machine learning. The hybrid control system, employing traditional nonlinear control methods integrated with machine learning models, was designed for 20 degrees of freedom. The robotic platform predicted and optimized robotic movements and compared favorably with the theoretical results. The work presents a foundational framework for future research into intelligent control systems, requiring precision and adaptability for industrial applications. In the work of [PERSON] and [PERSON] (Contribution 3) on decoding urban intelligence, considering clustering and feature importance in smart cities, datasets from the Smart City Index (SCI) were used to analyze and rank smart cities globally. The authors apply unsupervised learning models to cluster cities, leveraging their smartness indices, and employ supervised learning models such as random forests, support vector machines (SVMs), and others to ascertain the relevance of critical features that influence the smartness of a city. The results show that smart living is crucial to the smartness of a city. Other key indicators are smart mobility and smart environment. The study clarifies what makes a city'smart' and provides a comprehensive framework for policymakers to improve urban living standards. In Contribution 4, [PERSON] et al. propose a hybrid cryptography scheme for securing critical user information over the Internet of Medical Things platforms. The proposed IoMT-based model comprising the modified Caesar cipher combined with the Elliptic Curve Diffie Hellman (ECDH) and Digital Signature Algorithm (DSA) preserves the privacy of the data, is resistant to various cryptanalysis attacks, and is less computationally intensive. The results indicated that the modified algorithm secures messages during transmission, performs key exchanges between clients and healthcare centers, and guarantees user authentication. [PERSON] et al. (Contribution 5) identified risk factors using the adaptive neuro-fuzzy inference system (ANFIS)-based security risk assessment model in each stage of the software development life cycle (SDLC). The study identifies and validates the risk factors required to assess security risk at each phase of SDLC reliably. The Software Risk Assessment (SRA) model based on the ANFIS was applied to each phase of the SDLC, with the identified risk factors as inputs to achieve a logical representation of the fuzzification process. In the model design, two triangular membership functions were taken to represent the risk factor of each label. In comparison, four membership functions were employed to characterize the labels of the target SRA value.