Secure AI Model Training for Smart Cities Without Sharing Your Private Data

What is it about?

This work introduces a new system that helps devices in smart cities—like traffic sensors, surveillance cameras, and smart meters—collaborate to train artificial intelligence (AI) models without ever sharing their private data. The approach is based on federated learning, where devices learn from data locally and only share model updates instead of raw information. However, existing federated learning methods can still face privacy, security, and trust challenges, especially when scaled to large, complex Internet of Things (IoT) environments like smart cities. To solve these problems, the authors propose a secure framework called FL-DABE-BC. It combines multiple cutting-edge technologies: ☞ Decentralized Attribute-Based Encryption (DABE) allows only authorised devices to access data or communicate. ☞ Homomorphic Encryption (HE) ensures computations can be done directly on encrypted data. ☞ Secure Multi-Party Computation (SMPC) allows devices to collaborate securely without revealing their private inputs. ☞ Blockchain maintains a tamper-proof, transparent record of all interactions between devices. The proposed framework works across edge devices (like cameras), fog nodes (like local servers), and cloud platforms. It secures the entire learning process—from data collection to AI model updates—using encryption and formal authentication logic. It also handles threats like data tampering, eavesdropping, impersonation, and even man-in-the-middle attacks, all while ensuring fast and scalable model training. In short, this paper presents a privacy-preserving, decentralised AI training framework tailored for the real-world complexity and security needs of smart cities.

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Photo by Marc-Olivier Jodoin on Unsplash

Photo by Marc-Olivier Jodoin on Unsplash

Why is it important?

This work is timely and significant because smart cities and IoT systems are expanding rapidly, raising serious concerns about data privacy, cybersecurity, and infrastructure trustworthiness. Traditional machine learning models require sensitive data to be sent to central servers, making them vulnerable to attacks and misuse. While federated learning helps address this, it is still susceptible to attacks if not properly secured. What sets this work apart is its comprehensive and multi-layered security design. Unlike existing models, FL-DABE-BC: ☞ Combines multiple encryption and privacy-preserving techniques into a single, cohesive framework. ☞ Provides decentralised authentication to avoid single points of failure. ☞ Uses blockchain to offer transparent, immutable logs of all data interactions, fostering trust among devices. ☞ Is designed for real-time, large-scale IoT systems, making it more practical and adaptable than many academic-only models. Its contributions lie in creating a robust solution to real-world privacy and security threats that smart cities are already facing. The system is scalable, flexible, and secure enough to be deployed across various domains—like traffic systems, public safety, energy management, and healthcare—where sensitive data must be protected without hindering AI progress.

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