mlsanityData to 7 Oct 2026

OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning

arXiv 2107.05252 ↗First posted 12 Jul 2021cs.CR cs.DC cs.LGcited by 9

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Abstract (arXiv)

We propose OmniLytics, a blockchain-based secure data trading marketplace for machine learning applications. Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train an ML model requested by some model owners, and receive compensation for data contribution. OmniLytics enables such model training while simultaneously providing 1) model security against curious data owners; 2) data security against the curious model and data owners; 3) resilience to malicious data owners who provide faulty results to poison model training; and 4) resilience to malicious model owners who intend to evade payment. OmniLytics is implemented as a blockchain smart contract to guarantee the atomicity of payment. In OmniLytics, a model owner splits its model into the private and public parts and publishes the public part on the contract. Through the execution of the contract, the participating data owners securely aggregate their locally trained models to update the model owner's public model and receive reimbursement through the contract. We implement a working prototype of OmniLytics on Ethereum blockchain and perform extensive experiments to measure its gas cost, execution time, and model quality under various parameter combinations. For training a CNN on the MNIST dataset, the MO is able to boost its model accuracy from 62% to 83% within 500ms in blockchain processing time.This demonstrates the effectiveness of OmniLytics for practical deployment.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 23 of its 47 references that are in the map. First posted on arXiv 12 Jul 2021.

Builds on

23 of 47 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Deep Leakage from Gradientscited by 1,004
  3. 2018How To Backdoor Federated Learningcited by 745
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  5. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  6. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  7. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  8. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  9. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  10. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  11. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
  12. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 242
11 more
  1. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 148
  2. 2018Blockchained On-Device Federated Learningcited by 105
  3. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  4. 2020FedCoin: A Peer-to-Peer Payment System for Federated Learningcited by 39
  5. 2020When Federated Learning Meets Blockchain: A New Distributed Learning Paradigmcited by 31
  6. 2020Byzantine-Resilient High-Dimensional Federated Learningcited by 27
  7. 2017Byzantine-Tolerant Machine Learningcited by 18
  8. 2019Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approachcited by 13
  9. 2018IDMoB: IoT Data Marketplace on Blockchaincited by 5
  10. 2018Blockchain Enabled Data Marketplace -- Design and Challengescited by 2
  11. 2018Achieving Data Truthfulness and Privacy Preservation in Data Marketscited by 2

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Cited by year

Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 5.

9 papers.

  1. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 63
  2. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  3. 2024Task-Agnostic Detector for Insertion-Based Backdoor Attackscited by 23
  4. 2023Model Extraction Attacks Revisitedcited by 21
  5. 2024Blockchain for Large Language Model Security and Safety: A Holistic Surveycited by 8
  6. 2023Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchaincited by 5
  7. 2024A Review on Building Blocks of Decentralized Artificial Intelligencecited by 2
  8. 2024Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)cited by 0
  9. 2024Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)cited by 0