Verifiable and Privacy-Preserving Decentralized Collaboration for Machine Learning Model Improvement

Decentralized collaboration, particularly in complex domains like machine learning (ML) model development, faces hurdles regarding trust, intellectual property (IP) protection, and the verification of contributions. Traditional centralized platforms introduce bottlenecks, while existing decentralized approaches often lack mechanisms to privately verify complex, computationally intensive work. This paper introduces a framework integrating Zero-Knowledge Proofs (ZKPs) and smart contracts to enable trustless, verifiable, and privacy-preserving collaborative ML model improvement process. Our system allows "Improvers" to cryptographically prove superior model performance compared to a baseline, without revealing proprietary model parameters prematurely. The framework features on-chain job management, client-side ZKP generation, on-chain verification, automated selection of the best contributor based on verified proofs, secure solution submission via cryptographic commitments, and an arbiter-based dispute resolution mechanism. We analyze different ZKP workflow variations, evaluating performance and suitability. Performance analysis demonstrates the feasibility of client-side proof generation for individual models, while highlighting the resource demands of proof aggregation, suggesting its suitability for server-side work. On-chain gas cost evaluation indicates the system's economic viability for Layer 2 (L2) scaling solution deployments under specific cost assumptions. This work provides a first step for secure and verifiable collaboration in ML and potentially other software development tasks in decentralized environments.