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

Aug 3, 2026

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Distributed machine learning accelerates training by distributing computation across multiple workers, but releasing final model weights can trigger disputes over who owns and actually contributed to the trained parameters. Ensuring computational integrity and usefulness of workers’ computations is therefore essential to prevent failures and malicious tampering. This work introduces a binary linear tree commitment-based ownership protection model with low overhead and compact proofs. An inner-product-argument approach enables efficient proof aggregation, while identity-key watermarking prevents forged or duplicated commitments. Performance analysis and comparisons with SNARK-based hash commitments confirm the proposed model’s effectiveness in preserving integrity.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Introduction

Motivation: ownership disputes in distributed training

Threat model and integrity requirements

Background and related verification methods

Cryptographic primitives and proof systems

Binary Linear Tree Commitment-based Ownership Protection for Distributed Machine Learning Tianxiu Xie [3120215672@bit.edu.cn](3120215672@bit.edu.cn)[ ](3120215672@bit.edu.cn)Beijing Institute of Technology China Keke Gai [gaikeke@bit.edu.cn](gaikeke@bit.edu.cn)[ ](gaikeke@bit.edu.cn)Beijing Institute of Technology China arXiv :2401 .05895v1 [ cs .LG] 11 Jan 2024 Jing Yu [yujing02@iie.ac.cn](yujing02@iie.ac.cn) Institute of Information Engineering, CAS China Liehuang Zhu [liehuangz@bit.edu.cn](liehuangz@bit.edu.cn)[ ](liehuangz@bit.edu.cn)Beijing Institute of Technology China ABSTRACT 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. CCS CONCEPTS • Security and privacy → Digital rights management; • Computing methodologies → Neural networks; Distributed algorithms. KEYWORDS Distributed Identity Audit, Deep Neural Network, Model Ownership Verification, Blockchain, Intellectual Property Protection Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org). Conference acronym ’XX, June 03–05, 2018, Woodstock, NY © 2018 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 978-1-4503-XXXX-X/18/06. . . $15.00 [https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX) ACM Reference Format: Tianxiu Xie, Keke Gai, Jing Yu, and Liehuang Zhu. 2018. Binary Linear Tree Commitment-based Ownership Protection for Distributed Machine Learning. In Proceedings of Make sure to enter the correct conference title from your rights confirmation emai (Conference acronym ’XX). ACM, New York, NY, USA, 9 pages. [https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX) 1 INTRODUCTION High-performance machine learning models typically involve expensive iterative optimization, owing mainly to the requirements for voluminous labeled data and extensive computing resources. To reduce overall training costs, Distributed Machine Learning (DML) allows model owners to outsource training tasks to multiple workers [34] . Until the model converges, each worker is required to s

FAQs

Why do model ownership disputes arise in distributed machine learning?

Workers submit local weights each epoch and, after convergence, final parameters are released. Because training architectures can be public and many parties contribute during optimization, workers may struggle to prove their specific contribution to the final model.

What problem does the proposed commitment-based model aim to solve?

It targets ownership disputes and prevents accidental failures and malicious attacks by verifying computational integrity and the effectiveness of workers’ computations with limited overhead and concise proofs.

How does the method make proofs efficient and resistant to forgery?

A maintainable binary linear tree structure reduces proof-update costs during frequent parameter changes, and the scheme uses inner product arguments for efficient proof aggregation. Additionally, proofs of model weights are watermarked with worker identity keys to block forged or duplicated commitments.

Aug 3, 2026

Upload by Hazel