# FL-Market: Trading Private Models in Federated Learning

arXiv: https://arxiv.org/abs/2106.04384 · HTML: https://mlsanity.com/paper/2106.04384/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 91 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 30 of 45)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [Differentiable Convex Optimization Layers](https://mlsanity.com/paper/1910.12430/) (2019)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [iDLG: Improved Deep Leakage from Gradients](https://mlsanity.com/paper/2001.02610/) (2020)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [A Rewriting System for Convex Optimization Problems](https://mlsanity.com/paper/1709.04494/) (2017)
- [Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity](https://mlsanity.com/paper/1811.12469/) (2018)
- [OSQP: An Operator Splitting Solver for Quadratic Programs](https://mlsanity.com/paper/1711.08013/) (2017)
- [Mitigating Sybils in Federated Learning Poisoning](https://mlsanity.com/paper/1808.04866/) (2018)
- [Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms](https://mlsanity.com/paper/1908.08619/) (2019)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [Federated Learning for Emoji Prediction in a Mobile Keyboard](https://mlsanity.com/paper/1906.04329/) (2019)
- [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019)
- [A Marketplace for Data: An Algorithmic Solution](https://mlsanity.com/paper/1805.08125/) (2018)
- [Local Differential Privacy based Federated Learning for Internet of Things](https://mlsanity.com/paper/2004.08856/) (2020)
- [LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2007.15789/) (2020)
- [IBM Federated Learning: an Enterprise Framework White Paper V0.1](https://mlsanity.com/paper/2007.10987/) (2020)
- [Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach](https://mlsanity.com/paper/1905.07479/) (2019)
- [Motivating Workers in Federated Learning: A Stackelberg Game Perspective](https://mlsanity.com/paper/1908.03092/) (2019)
- [Differentially Private Meta-Learning](https://mlsanity.com/paper/1909.05830/) (2019)
- [Toward an Automated Auction Framework for Wireless Federated Learning Services Market](https://mlsanity.com/paper/1912.06370/) (2019)
- [Model-based Pricing for Machine Learning in a Data Marketplace](https://mlsanity.com/paper/1805.11450/) (2018)
- [FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection](https://mlsanity.com/paper/2003.10637/) (2020)

## Built on by (most cited first, 9 of 9)

- [BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning](https://mlsanity.com/paper/2305.05221/) (2023)
- [On the Effect of Defections in Federated Learning and How to Prevent Them](https://mlsanity.com/paper/2311.16459/) (2023)
- [A Survey on Contribution Evaluation in Vertical Federated Learning](https://mlsanity.com/paper/2405.02364/) (2024)
- [LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication](https://mlsanity.com/paper/2401.02675/) (2024)
- [FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning](https://mlsanity.com/paper/2411.11713/) (2024)
- [A Bargaining-based Approach for Feature Trading in Vertical Federated Learning](https://mlsanity.com/paper/2402.15247/) (2024)
- [Data Overvaluation Attack and Truthful Data Valuation in Federated Learning](https://mlsanity.com/paper/2502.00494/) (2025)
- [CARE: Compatibility-Aware Incentive Mechanisms for Federated Learning with Budgeted Requesters](https://mlsanity.com/paper/2504.15847/) (2025)
- [Data Valuation and Selection in a Federated Model Marketplace](https://mlsanity.com/paper/2509.18104/) (2025)

## Abstract (arXiv)

The difficulty in acquiring a sufficient amount of training data is a major bottleneck for machine learning (ML) based data analytics. Recently, commoditizing ML models has been proposed as an economical and moderate solution to ML-oriented data acquisition. However, existing model marketplaces assume that the broker can access data owners' private training data, which may not be realistic in practice. In this paper, to promote trustworthy data acquisition for ML tasks, we propose FL-Market, a locally private model marketplace that protects privacy not only against model buyers but also against the untrusted broker. FL-Market decouples ML from the need to centrally gather training data on the broker's side using federated learning, an emerging privacy-preserving ML paradigm in which data owners collaboratively train an ML model by uploading local gradients (to be aggregated into a global gradient for model updating). Then, FL-Market enables data owners to locally perturb their gradients by local differential privacy and thus further prevents privacy risks. To drive FL-Market, we propose a deep learning-empowered auction mechanism for intelligently deciding the local gradients' perturbation levels and an optimal aggregation mechanism for aggregating the perturbed gradients. Our auction and aggregation mechanisms can jointly maximize the global gradient's accuracy, which optimizes model buyers' utility. Our experiments verify the effectiveness of the proposed mechanisms.