The Internet of Things (IoT) has revolutionised modern society by connecting billions of devices across various sectors, including smart homes, healthcare, transportation, and industrial systems. However, this connectivity introduces critical security and privacy challenges. This Topical Collection focuses on three transformative technologies—blockchain, differential privacy, and federated intelligence—that address these challenges.

Blockchain provides decentralized, tamper-proof infrastructures for IoT data integrity and device authentication. Differential privacy offers mathematically rigorous privacy guarantees for IoT data analytics while preserving utility. Federated intelligence enables collaborative machine learning across IoT devices without requiring centralization of sensitive data.

This collection aims to showcase cutting-edge research that integrates these technologies, addresses implementation challenges such as energy efficiency and scalability, and explores real-world applications in smart cities, Industrial IoT, healthcare, and vehicular networks. It will serve as a comprehensive resource for researchers and practitioners developing secure, privacy-preserving IoT ecosystems.

This Collection supports and amplifies research related to SDG3, SDG 9 and SDG11.

Keywords: Blockchain for IoT; Differential Privacy; Federated Learning; IoT Security; Privacy-Preserving IoT; Distributed Ledger Technology; Edge Intelligence; Secure Smart Systems; Federated Intelligence; IoT Data Privacy

The Internet of Things (IoT) has emerged as a transformative paradigm that interconnects billions of smart devices, sensors, and systems to enable seamless data exchange and intelligent automation. With widespread adoption across critical sectors including smart cities, industrial automation, healthcare, transportation, and smart homes, IoT delivers substantial economic and social value by enhancing operational efficiency, service personalization, and resource optimization. However, the exponential growth of IoT ecosystems—characterized by device heterogeneity, distributed architectures, and massive data transmission—poses unprecedented challenges in ensuring information security (e.g., data privacy, access control, and threat mitigation) and developing robust machine learning models for real-time analytics, anomaly detection, and adaptive decision-making. To address these issues, researchers are exploring innovative methodologies such as federated learning, lightweight cryptography, and AI-driven threat intelligence. The interdisciplinary nature of IoT research necessitates collaboration among experts in computer science, cybersecurity, data science, and electrical engineering, whose collective insights drive technological breakthroughs and risk mitigation strategies. In summary, IoT is a rapidly evolving field where machine learning and information security are foundational pillars, holding immense potential to unlock safe, reliable, and innovative applications that reshape industries and daily life.

This collection aims to bring together researchers and practitioners from academia and industry to present their latest findings, discuss recent advances, and exchange ideas on machine learning and information security for IoT. The collection will focus on the development of novel models, control strategies, learning techniques, and security methods for IoT, with an emphasis on their application to real-world problems. Analytical, numerical, and experimental works which contribute to the development of machine learning and information security for IoT, are welcome.

This Collection supports and amplifies research related to SDG11.

Keywords: Machine Learning; Information Security; Internet of Things; Control Systems; Signal Processing; Mathematical Optimization; Complex Networks; Big Data and Data Mining; Modelling and Simulation; Neural Networks