Secure verifiable aggregation for blockchain-based federated averaging

IoT devices’storage and computation capacities are constantly increasing in recent years,which brings critical challenges in data privacy protection.Federated learning(FL)and blockchain technology are two popular tech-niques used in IoT data aggregation,where FL enables data training with privacy protection,and blockchain provides a decentralized architecture for data storage and mining.However,very few the state-of-the-art works consider the applicability of the combination of FL and blockchain.In this paper,we adopt the federated aver-aging algorithm to reduce the communication overhead between the blockchain and end users to achieve higher performance.We also apply the double-mask-then-encrypt approach for end users to submit their local updates in order to protect data privacy.Finally,we propose and implement a non-interactive Public Verifiable Secret Sharing(PVSS)algorithm with Distributed Hash Table(DHT)that solves the user-drop-out problem and improves the communication efficiency between blockchain and end-users.At last,we theoretically analyze the security strengths of the proposed solution and conduct experiments to measure the execution time of PVSS on both the server and clients sides.