# Binary Linear Tree Commitment-based Ownership Protection for Distributed Machine Learning

arXiv: https://arxiv.org/abs/2401.05895 · HTML: https://mlsanity.com/paper/2401.05895/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018)
- [Minibatch vs Local SGD for Heterogeneous Distributed Learning](https://mlsanity.com/paper/2006.04735/) (2020)
- [Proof-of-Learning: Definitions and Practice](https://mlsanity.com/paper/2103.05633/) (2021)
- [IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary](https://mlsanity.com/paper/1910.12903/) (2019)
- [Defending against Model Stealing via Verifying Embedded External Features](https://mlsanity.com/paper/2112.03476/) (2021)
- [SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders](https://mlsanity.com/paper/2201.11692/) (2022)
- [Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks](https://mlsanity.com/paper/2110.06904/) (2021)
- [How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection](https://mlsanity.com/paper/2206.14157/) (2022)
- [Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets](https://mlsanity.com/paper/1902.03785/) (2019)

## Abstract (arXiv)

Distributed machine learning enables parallel training of extensive datasets by delegating computing tasks across multiple workers. Despite the cost reduction benefits of distributed machine learning, the dissemination of final model weights often leads to potential conflicts over model ownership as workers struggle to substantiate their involvement in the training computation. To address the above ownership issues and prevent accidental failures and malicious attacks, verifying the computational integrity and effectiveness of workers becomes particularly crucial in distributed machine learning. In this paper, we proposed a novel binary linear tree commitment-based ownership protection model to ensure computational integrity with limited overhead and concise proof. Due to the frequent updates of parameters during training, our commitment scheme introduces a maintainable tree structure to reduce the costs of updating proofs. Distinguished from SNARK-based verifiable computation, our model achieves efficient proof aggregation by leveraging inner product arguments. Furthermore, proofs of model weights are watermarked by worker identity keys to prevent commitments from being forged or duplicated. The performance analysis and comparison with SNARK-based hash commitments validate the efficacy of our model in preserving computational integrity within distributed machine learning.