# Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning

arXiv: https://arxiv.org/abs/2305.06784 · HTML: https://mlsanity.com/paper/2305.06784/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 11 May 2023.

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

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [FedVision: An Online Visual Object Detection Platform Powered by Federated Learning](https://mlsanity.com/paper/2001.06202/) (2020)
- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC](https://mlsanity.com/paper/2002.09699/) (2020)
- [An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach](https://mlsanity.com/paper/2009.10269/) (2020)
- [Toward an Automated Auction Framework for Wireless Federated Learning Services Market](https://mlsanity.com/paper/1912.06370/) (2019)
- [Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising](https://mlsanity.com/paper/1803.02194/) (2018)
- [Statistical Arbitrage Mining for Display Advertising](https://mlsanity.com/paper/1506.03837/) (2015)
- [Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraint](https://mlsanity.com/paper/2201.09047/) (2022)
- [Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement](https://mlsanity.com/paper/2201.02410/) (2022)

## Built on by (most cited first, 7 of 7)

- [Advances and Open Challenges in Federated Foundation Models](https://mlsanity.com/paper/2404.15381/) (2024)
- [Ten Challenging Problems in Federated Foundation Models](https://mlsanity.com/paper/2502.12176/) (2025)
- [Intelligent Agents for Auction-based Federated Learning: A Survey](https://mlsanity.com/paper/2404.13244/) (2024)
- [Multi-Session Budget Optimization for Forward Auction-based Federated Learning](https://mlsanity.com/paper/2311.12548/) (2023)
- [Federated Learning Incentive Mechanism under Buyers' Auction Market](https://mlsanity.com/paper/2309.05063/) (2023)
- [Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning](https://mlsanity.com/paper/2310.02651/) (2023)
- [Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learning](https://mlsanity.com/paper/2405.05991/) (2024)

## Abstract (arXiv)

Auction-based Federated Learning (AFL) has attracted extensive research interest due to its ability to motivate data owners to join FL through economic means. Existing works assume that only one data consumer and multiple data owners exist in an AFL marketplace (i.e., a monopoly market). Therefore, data owners bid to join the data consumer for FL. However, this assumption is not realistic in practical AFL marketplaces in which multiple data consumers can compete to attract data owners to join their respective FL tasks. In this paper, we bridge this gap by proposing a first-of-its-kind utility-maximizing bidding strategy for data consumers in federated learning (Fed-Bidder). It enables multiple FL data consumers to compete for data owners via AFL effectively and efficiently by providing with utility estimation capabilities which can accommodate diverse forms of winning functions, each reflecting different market dynamics. Extensive experiments based on six commonly adopted benchmark datasets show that Fed-Bidder is significantly more advantageous compared to four state-of-the-art approaches.