# FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC

arXiv: https://arxiv.org/abs/2002.09699 · HTML: https://mlsanity.com/paper/2002.09699/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2020.

## Built on by (most cited first, 30 of 48)

- [GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning](https://mlsanity.com/paper/2109.02053/) (2021)
- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [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)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs](https://mlsanity.com/paper/2309.01172/) (2023)
- [Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective](https://mlsanity.com/paper/2106.11814/) (2021)
- [Faithful Edge Federated Learning: Scalability and Privacy](https://mlsanity.com/paper/2106.15905/) (2021)
- [MarS-FL: Enabling Competitors to Collaborate in Federated Learning](https://mlsanity.com/paper/2110.13464/) (2021)
- [Data-Free Evaluation of User Contributions in Federated Learning](https://mlsanity.com/paper/2108.10623/) (2021)
- [Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models](https://mlsanity.com/paper/2302.04464/) (2023)
- [FedToken: Tokenized Incentives for Data Contribution in Federated Learning](https://mlsanity.com/paper/2209.09775/) (2022)
- [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)
- [FedDebug: Systematic Debugging for Federated Learning Applications](https://mlsanity.com/paper/2301.03553/) (2023)
- [Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement](https://mlsanity.com/paper/2201.02410/) (2022)
- [Incentivizing Federated Learning](https://mlsanity.com/paper/2205.10951/) (2022)
- [Joint Optimization of Energy Consumption and Completion Time in Federated Learning](https://mlsanity.com/paper/2209.14900/) (2022)
- [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)
- [ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning](https://mlsanity.com/paper/2405.20975/) (2024)
- [Federated Learning: Issues in Medical Application](https://mlsanity.com/paper/2109.00202/) (2021)
- [Intelligent Agents for Auction-based Federated Learning: A Survey](https://mlsanity.com/paper/2404.13244/) (2024)
- [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)
- [IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content](https://mlsanity.com/paper/2406.08526/) (2024)
- [Reward-Based 1-bit Compressed Federated Distillation on Blockchain](https://mlsanity.com/paper/2106.14265/) (2021)

## Abstract (arXiv)

Promising federated learning coupled with Mobile Edge Computing (MEC) is considered as one of the most promising solutions to the AI-driven service provision. Plenty of studies focus on federated learning from the performance and security aspects, but they neglect the incentive mechanism. In MEC, edge nodes would not like to voluntarily participate in learning, and they differ in the provision of multi-dimensional resources, both of which might deteriorate the performance of federated learning. Also, lightweight schemes appeal to edge nodes in MEC. These features require the incentive mechanism to be well designed for MEC. In this paper, we present an incentive mechanism FMore with multi-dimensional procurement auction of K winners. Our proposal FMore not only is lightweight and incentive compatible, but also encourages more high-quality edge nodes with low cost to participate in learning and eventually improve the performance of federated learning. We also present theoretical results of Nash equilibrium strategy to edge nodes and employ the expected utility theory to provide guidance to the aggregator. Both extensive simulations and real-world experiments demonstrate that the proposed scheme can effectively reduce the training rounds and drastically improve the model accuracy for challenging AI tasks.