Matrix Commitment-Based Ownership Verification for Distributed Machine Learning

Distributed Machine Learning (DML, where multiple computers train an AI model in parallel) creates challenges in determining which clients contributed what to the final model and preventing dishonest clients from falsely claiming ownership. This paper introduces MAMMON, a system using matrix commitments (a cryptographic method for verifying that data hasn't been tampered with) to verify each client's training history and establish fair ownership of the resulting model while using less computing power than existing methods.