Blockchain-based Asynchronous Federated Learning for Internet of Things

As an emerging distributed machine learning (ML) technology, federated learning can protect data privacy through collaborative learning AI models across a large number of IoT devices. However, inefficiency and vulnerability to poisoning attacks have slowed federated learning performance. To solve the above problems, a blockchain-based asynchronous federated learning framework (BAFL) is proposed to pursue both the security and efficiency. Blockchain ensures that data cannot be tampered with and secured while the asynchrony of learning speeds up global aggregation. In further, we propose the concept of device's score and use entropy weight method to measure the quality of model update. The score design directly determines the proportion of the device's model in the global aggregation and the allowed local update delay. By analyzing the optimal block generation rate, the paper also balances the equipment energy consumption and local update delay by adjusting local training delay and communication delay. The extensive evaluation results show that the proposed BAFL framework has performs better in the aspects of both efficiency and anti-poisoning attacks than other distributed ML methods.