Federated Learning Meets Blockchain: Building Trusted AI Networks for Smart Cities

A new wave of research combines federated learning with blockchain, creating decentralized AI models that enhance security, trust, and efficiency for IoT and smart city networks.

Artificial intelligence is becoming increasingly integrated with the Internet of Things (IoT), powering applications in transportation, energy grids, and healthcare. However, traditional AI models rely on centralized training, raising concerns about data privacy, security, and trust.

Federated learning (FL) offers an alternative by allowing AI models to be trained collaboratively across multiple devices without sharing raw data. Still, FL systems face risks of data poisoning, manipulation, and lack of accountability. Blockchain technology addresses these issues by providing a tamper-resistant ledger for recording model updates, ensuring transparency in the training process.

Researchers from various institutes have proposed blockchain-FL frameworks that integrate incentive mechanisms for participants, secure aggregation of updates, and consensus-driven validation of model improvements. This creates a trusted environment where thousands of IoT devices can train AI models collectively while minimizing risks.

Such systems are being considered essential for 6G networks and smart city infrastructure, where billions of devices must interact seamlessly. By merging blockchain’s trust with AI’s intelligence, these solutions could redefine how future societies manage data.

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