Blockchain-Based Federated Learning for Privacy-Preserving AI in Smart City
Keywords:
Smart Cities, Federated Learning, Blockchain, Privacy-Preserving AI, IoT, Edge Computing, Proof-of-AuthorityAbstract
Artificial Intelligence (AI) becomes more and more common in infrastructures of Smart Cities with the use of AI to optimize traffic, manage energy consumption, and monitor the environment. There are however major concerns that come with centralized AI training such as privacy risk, governance of data, and single points of failure. The paper introduces a blockchain-enabled federated learning (hereinafter, BFL) framework to organize the training of AI models to ensure privacy and decentralization of training processes on heterogeneous internet of things (IoT) devices located in the urban setting. The framework uses a lightweight Proof-of-Authority (PoA) blockchain to securely, tamper-proof aggregate and log transparent participation and actively uses adaptive compression techniques in model communication and storage costs. An incentive mechanism can be set up with the help of a smart contract to welcome a wider range of stakeholders in the city. Experimental analysis conducted on the METR-LA traffic dataset shows that BFL can attain similar performance evaluation to the centralized methods whilst having lower privacy leakage risks, possessing low blockchain latency (<200 ms), and saving 45 percent in communication expense. The suggested framework gives a scalable and trustworthy AI training scheme to intelligent cities.
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