FedChain: An Efficient and Secure Consensus Protocol based on Proof of Useful Federated Learning for Blockchain

TL;DR

FedChain introduces a novel blockchain consensus protocol leveraging federated learning as a useful proof of work, enhancing energy efficiency and privacy while maintaining security through innovative cryptographic techniques.

Contribution

The paper presents a new blockchain consensus protocol based on federated learning, integrating privacy-preserving mechanisms and efficiency improvements over traditional PoW methods.

Findings

FedChain reduces energy consumption compared to PoW.

The protocol maintains high security and privacy for participants.

Experimental results validate the effectiveness of the proposed approach.

Abstract

Blockchain has become a popular decentralized paradigm for various applications in the zero-trust environment. The core of the blockchain is the consensus protocol, which establishes consensus among all the participants. PoW (Proof-of-Work) is one of the most popular consensus protocols. However, the PoW consensus protocol which incentives the participants to use their computing power to solve a meaningless hash puzzle is continuously questioned as energy-wasting. To address these issues, we propose an efficient and secure consensus protocol based on proof of useful federated learning for blockchain (called FedChain). We first propose a secure and robust blockchain architecture that takes federated learning tasks as proof of work. Then a pool aggregation mechanism is integrated to improve the efficiency of the FedChain architecture. To protect model parameter privacy for each participant within a mining pool, a secret sharing-based ring-all reduce architecture is designed. We also introduce a data distribution-based federated learning model optimization algorithm to improve the model performance of FedChain. At last, a zero-knowledge proof-based federated learning model verification is introduced to preserve the privacy of federated learning participants while proving the model performance of federated learning participants. Our approach has been tested and validated through extensive experiments, demonstrating its performance.