Privacy-preserving Aggregation Scheme for Blockchained Federated Learning in IoT

The rapid increase in Internet of Things (IoT) connected devices and generated data has greatly promoted the development of artificial intelligence. Federated Learning (FL) provides strong support for the data-driven society by training the model locally without leaking the privacy of the client. However, due to the unreliability of IoT devices and the centralized training model of FL, there are still security risks such as privacy leakage in federated learning. Therefore, this paper proposes a decentralized and privacy-preserving aggregation scheme, named DPPA, for FL. DPPA leverages the blockchain to structure a decentralized FL architecture, and leverages Paillier cryptosystem to realize the privacy protection of local data and the safe aggregation of model parameters. Through security analysis, we demonstrate that DPPA resists various security threats and preserve client privacy. Experiments show that in the large-scale IoT device environment, DPPA has great advantages over the existing competing approaches in terms of computational overhead.