SCS-RDL: A Smart Contract Scheme for Rational Delegation Learning

Abstract

Delegation learning, as a privacy-preserving machine learning paradigm, has been widely applied in large-scale data processing and complex computational tasks in recent years. However, existing schemes still have deficiencies in guarding against malicious behavior and verifying result correctness, and they struggle to balance security and efficiency across different adversary models. In this paper, We propose a rational delegation learning smart contract scheme (SCS-RDL). First, it combines game theory to construct a rational delegation learning framework and introduces a blockchain-based probabilistic verification method. Second, we construct a smart contract scheme and design rational utility functions that effectively incentivize participants’ honest behavior. Finally, experiments demonstrate that SCS-RDL scheme could enhance delegation learning’s training efficiency without sacrificing accuracy and could satisfy public verifiability.