Matrix Commitment-based Ownership Verification for Distributed Machine Learning

Distributed Machine Learning (DML) enables parallel training but creates conflicts regarding model ownership determination, as verifying the specific contributions of individual clients during weight updates remains a challenge. To resolve ownership disputes and prevent malicious clients from forging data, verifying the integrity and effectiveness of each client's unique training history can fairly establish their partial ownership of the final DML model. In this paper, for the first time we propose a novel Matrix Commitment-based DML Ownership Verification (MAMMON) scheme that adopts a concise proof to ensure computational integrity and correctness within a limited computing cost. In contrast to computationally intensive SNARK-based approaches, MAMMON constructs a multi-linear tree structure to reduce proof update costs and avoids complex arithmetic circuit computations by standardizing the training process. Additionally, we watermark the weight proofs using client identity private keys to safeguard the commitments against tampering or unauthorized acquisition. Extensive experiments demonstrate MAMMON's superior performance in preserving computational integrity of DML training, covering both theoretical analysis and comparisons with other existing baselines.