mlsanityData to 7 Oct 2026

An Auction-based Marketplace for Model Trading in Federated Learning

arXiv 2402.01802 ↗First posted 2 Feb 2024cs.LG cs.AI cs.GTcited by 2

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) is increasingly recognized for its efficacy in training models using locally distributed data. However, the proper valuation of shared data in this collaborative process remains insufficiently addressed. In this work, we frame FL as a marketplace of models, where clients act as both buyers and sellers, engaging in model trading. This FL market allows clients to gain monetary reward by selling their own models and improve local model performance through the purchase of others' models. We propose an auction-based solution to ensure proper pricing based on performance gain. Incentive mechanisms are designed to encourage clients to truthfully reveal their model valuations. Furthermore, we introduce a reinforcement learning (RL) framework for marketing operations, aiming to achieve maximum trading volumes under the dynamic and evolving market status. Experimental results on four datasets demonstrate that the proposed FL market can achieve high trading revenue and fair downstream task accuracy.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 53 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

32 of 53 references.

References · 32

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  4. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  5. 2018LEAF: A Benchmark for Federated Settingscited by 780
  6. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  7. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  8. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  9. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  10. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  11. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  12. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
20 more
  1. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  2. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  3. 2023Stochastic Controlled Averaging for Federated Learning with Communication Compressioncited by 100
  4. 2018Federated Learning for Keyword Spottingcited by 95
  5. 2021Towards Fairness-Aware Federated Learningcited by 57
  6. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
  7. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  8. 2020FedCoin: A Peer-to-Peer Payment System for Federated Learningcited by 39
  9. 2020A Survey on Data Pricing: from Economics to Data Sciencecited by 39
  10. 2022Mechanisms that Incentivize Data Sharing in Federated Learningcited by 38
  11. 2021Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertisingcited by 31
  12. 2020Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approachcited by 29
  13. 2018Joint Service Pricing and Cooperative Relay Communication for Federated Learningcited by 22
  14. 2020Trading Data For Learning: Incentive Mechanism For On-Device Federated Learningcited by 21
  15. 2015True Online Temporal-Difference Learningcited by 16
  16. 2020An Exploratory Analysis on Users' Contributions in Federated Learningcited by 15
  17. 2020Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertisingcited by 13
  18. 2020A VCG-based Fair Incentive Mechanism for Federated Learningcited by 11
  19. 2020FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanismcited by 10
  20. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
  2. 2026Beyond Denial-of-Service: The Puppeteer's Attack for Fine-Grained Control in Ranking-Based Federated Learningcited by 1