# D2M: A Decentralized, Privacy-Preserving, Incentive-Compatible Data Marketplace for Collaborative Learning

arXiv: https://arxiv.org/abs/2512.10372 · HTML: https://mlsanity.com/paper/2512.10372/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [A Marketplace for Data: An Algorithmic Solution](https://mlsanity.com/paper/1805.08125/) (2018)
- [Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation](https://mlsanity.com/paper/2101.06905/) (2021)
- [martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture](https://mlsanity.com/paper/2309.01098/) (2023)
- [Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm](https://mlsanity.com/paper/2306.02543/) (2023)
- [YODA: Enabling computationally intensive contracts on blockchains with Byzantine and Selfish nodes](https://mlsanity.com/paper/1811.03265/) (2018)
- [Reliable, Fair and Decentralized Marketplace for Content Sharing Using Blockchain](https://mlsanity.com/paper/2009.11033/) (2020)
- [UnifyFL: Enabling Decentralized Cross-Silo Federated Learning](https://mlsanity.com/paper/2504.18916/) (2025)

## Abstract (arXiv)

The rising demand for collaborative machine learning and data analytics calls for secure and decentralized data sharing frameworks that balance privacy, trust, and incentives. Existing approaches, including federated learning (FL) and blockchain-based data markets, fall short: FL often depends on trusted aggregators and lacks Byzantine robustness, while blockchain frameworks struggle with computation-intensive training and incentive integration. We present prot, a decentralized data marketplace that unifies federated learning, blockchain arbitration, and economic incentives into a single framework for privacy-preserving data sharing. prot enables data buyers to submit bid-based requests via blockchain smart contracts, which manage auctions, escrow, and dispute resolution. Computationally intensive training is delegated to cone (ulineCompute ulineNetwork for ulineExecution), an off-chain distributed execution layer. To safeguard against adversarial behavior, prot integrates a modified YODA protocol with exponentially growing execution sets for resilient consensus, and introduces Corrected OSMD to mitigate malicious or low-quality contributions from sellers. All protocols are incentive-compatible, and our game-theoretic analysis establishes honesty as the dominant strategy. We implement prot on Ethereum and evaluate it over benchmark datasets -- MNIST, Fashion-MNIST, and CIFAR-10 -- under varying adversarial settings. prot achieves up to 99% accuracy on MNIST and 90% on Fashion-MNIST, with less than 3% degradation up to 30% Byzantine nodes, and 56% accuracy on CIFAR-10 despite its complexity. Our results show that prot ensures privacy, maintains robustness under adversarial conditions, and scales efficiently with the number of participants, making it a practical foundation for real-world decentralized data sharing.