TrustScale ML Verifiable Privacy Preserving Multi Node Training for Robust Model Development

Abstract

This paper proposes TrustScale ML: a verifiable and privacy-preserving framework for distributed ML to detect computing integrity, protect the confidentiality of gradients and enable scalable collaborative learning. We propose TrustScale ML, a scalable ML system integrating PoL, which enables the efficient verification of local ML computations, utilizing the CKKS homomorphic encryption scheme for the protection of gradients during distributed model training. The framework supports a variety of distributed learning settings, including data and model parallelism, centralized and decentralized optimization, and synchronous and asynchronous training. To improve trustworthy, security mechanisms should aim at integrity of the model, verification of computation, and protection against unauthorized access to sensitive learning information. The research describes the overall system architecture, communication and security protocols, verification workflow, and implementation methodology. Further, it provides a systematic evaluation agenda for evaluating correctness, robustness, privacy, computational overhead, and scalability across representative ML workloads. TrustScale ML provides a unified framework for trustworthy distributed learning systems through verifiable computation and privacy-preserving gradient processing. The suggested framework is aimed at guiding future empirical studies and practical application of secure, scalable and verifiable distributed ML systems in heterogeneous and possibly untrusted computing environments.