Abstract:

Most of the existing distributed learning schemes solve the problem of malicious nodes by adding a disciplinary mechanism to the protocol.This method is based on two assumptions:1.Participants give up the assumption of malicious behavior to maximize their own interests,and the calculation results can be verified only after the event occurs,which is not suitable for some scenarios requiring immediate verification;2.It is based on the assumption of a trusted third party.However,in practice,the credibility of the third party cannot be fully guaranteed.Using the trust mechanism of the blockchain,this paper proposes an anti malicious node scheme based on the smart contract,which realizes the whole process of model training in machine learning through the smart contract to ensure that the machine learning model is not damaged by malicious nodes.This scheme takes the distributed machine learning model based on secure multi-party computing as the research model,and uses the smart contract of the blockchain to realize the data sharing,verification and training process.All participants can only execute according to the specified protocol,converting all participants into semi sincere participants;At the same time,in order to solve the privacy problems brought by the open and transparent characteristics of the blockchain,ring signature is used to hide the data address of participants and protect the identity of participants.Experiments show that this scheme has great advantages in resisting malicious nodes compared with the traditional distributed machine learning model based on secure multi-party computing.

Key words: blockchain, smart contract, distributed learning, malicious nodes,secure multi-party computation, ring signature