# Toward an Automated Auction Framework for Wireless Federated Learning Services Market

arXiv: https://arxiv.org/abs/1912.06370 · HTML: https://mlsanity.com/paper/1912.06370/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

## Built on by (most cited first, 30 of 38)

- [Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks](https://mlsanity.com/paper/2303.15991/) (2023)
- [Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective](https://mlsanity.com/paper/2111.11850/) (2021)
- [A Comprehensive Survey of Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2106.15406/) (2021)
- [Advances and Open Challenges in Federated Foundation Models](https://mlsanity.com/paper/2404.15381/) (2024)
- [Faithful Edge Federated Learning: Scalability and Privacy](https://mlsanity.com/paper/2106.15905/) (2021)
- [FL-Market: Trading Private Models in Federated Learning](https://mlsanity.com/paper/2106.04384/) (2021)
- [A Snapshot of the Frontiers of Client Selection in Federated Learning](https://mlsanity.com/paper/2210.04607/) (2022)
- [Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation](https://mlsanity.com/paper/2304.07981/) (2023)
- [Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning](https://mlsanity.com/paper/2305.06784/) (2023)
- [Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraint](https://mlsanity.com/paper/2201.09047/) (2022)
- [Incentivizing Federated Learning](https://mlsanity.com/paper/2205.10951/) (2022)
- [Social-Aware Clustered Federated Learning with Customized Privacy Preservation](https://mlsanity.com/paper/2212.13992/) (2022)
- [Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling](https://mlsanity.com/paper/2302.00106/) (2023)
- [Artificial Intelligence for Web 3.0: A Comprehensive Survey](https://mlsanity.com/paper/2309.09972/) (2023)
- [Intelligent Agents for Auction-based Federated Learning: A Survey](https://mlsanity.com/paper/2404.13244/) (2024)
- [Strategic Coalition for Data Pricing in IoT Data Markets](https://mlsanity.com/paper/2206.07785/) (2022)
- [iDML: Incentivized Decentralized Machine Learning](https://mlsanity.com/paper/2304.05354/) (2023)
- [Exploring Data Redundancy in Real-world Image Classification through Data Selection](https://mlsanity.com/paper/2306.14113/) (2023)
- [Multi-Session Budget Optimization for Forward Auction-based Federated Learning](https://mlsanity.com/paper/2311.12548/) (2023)
- [Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets](https://mlsanity.com/paper/2208.04322/) (2022)
- [Economics of Semantic Communication System: An Auction Approach](https://mlsanity.com/paper/2208.05040/) (2022)
- [Federated Learning Incentive Mechanism under Buyers' Auction Market](https://mlsanity.com/paper/2309.05063/) (2023)
- [Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning](https://mlsanity.com/paper/2309.11722/) (2023)
- [Asynchronous Federated Learning with Incentive Mechanism Based on Contract Theory](https://mlsanity.com/paper/2310.06448/) (2023)
- [In Silico Sociology: Forecasting COVID-19 Polarization with Large Language Models](https://mlsanity.com/paper/2407.11190/) (2024)
- [Efficient Split Federated Learning for Large Language Models over Communication Networks](https://mlsanity.com/paper/2504.14667/) (2025)
- [Prior-Independent Auctions for the Demand Side of Federated Learning](https://mlsanity.com/paper/2103.14375/) (2021)
- [FedAgg: Adaptive Federated Learning with Aggregated Gradients](https://mlsanity.com/paper/2303.15799/) (2023)
- [An Incentive Mechanism for Federated Learning Based on Multiple Resource Exchange](https://mlsanity.com/paper/2312.08096/) (2023)
- [Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learning](https://mlsanity.com/paper/2405.05991/) (2024)

## Abstract (arXiv)

In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this paper, we propose an auction based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.