# Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

arXiv: https://arxiv.org/abs/1905.07479 · HTML: https://mlsanity.com/paper/1905.07479/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

## Built on by (most cited first, 30 of 45)

- [Federated Learning: Opportunities and Challenges](https://mlsanity.com/paper/2101.05428/) (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)
- [Mechanisms that Incentivize Data Sharing in Federated Learning](https://mlsanity.com/paper/2207.04557/) (2022)
- [A Comprehensive Survey of Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2106.15406/) (2021)
- [Game of Gradients: Mitigating Irrelevant Clients in Federated Learning](https://mlsanity.com/paper/2110.12257/) (2021)
- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2102.03401/) (2021)
- [Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards](https://mlsanity.com/paper/2112.09327/) (2021)
- [FedH2L: Federated Learning with Model and Statistical Heterogeneity](https://mlsanity.com/paper/2101.11296/) (2021)
- [A Contract Theory based Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2108.05568/) (2021)
- [AdaFed: Fair Federated Learning via Adaptive Common Descent Direction](https://mlsanity.com/paper/2401.04993/) (2024)
- [Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks](https://mlsanity.com/paper/2101.03627/) (2021)
- [FL-Market: Trading Private Models in Federated Learning](https://mlsanity.com/paper/2106.04384/) (2021)
- [Dynamic Attention-based Communication-Efficient Federated Learning](https://mlsanity.com/paper/2108.05765/) (2021)
- [Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective](https://mlsanity.com/paper/2207.12030/) (2022)
- [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)
- [On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator](https://mlsanity.com/paper/2108.02712/) (2021)
- [FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2207.10265/) (2022)
- [Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design](https://mlsanity.com/paper/2211.04132/) (2022)
- [FedDebug: Systematic Debugging for Federated Learning Applications](https://mlsanity.com/paper/2301.03553/) (2023)
- [Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks](https://mlsanity.com/paper/2502.04850/) (2025)
- [Incentivizing Federated Learning](https://mlsanity.com/paper/2205.10951/) (2022)
- [Distributed Quasi-Newton Method for Fair and Fast Federated Learning](https://mlsanity.com/paper/2501.10877/) (2025)
- [PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework](https://mlsanity.com/paper/2505.01866/) (2025)
- [Maximizing Global Model Appeal in Federated Learning](https://mlsanity.com/paper/2205.14840/) (2022)
- [Reward-Based 1-bit Compressed Federated Distillation on Blockchain](https://mlsanity.com/paper/2106.14265/) (2021)
- [A Framework for Incentivized Collaborative Learning](https://mlsanity.com/paper/2305.17052/) (2023)
- [eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference](https://mlsanity.com/paper/2205.05756/) (2022)

## Abstract (arXiv)

To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e.g., mobile devices, to distributedly train and globally share models without revealing their local data. This technique can not only significantly improve privacy protection for mobile devices, but also ensure good performance of the trained results collectively. Currently, most the existing studies focus on optimizing federated learning algorithms to improve model training performance. However, incentive mechanisms to motivate the mobile devices to join model training have been largely overlooked. The mobile devices suffer from considerable overhead in terms of computation and communication during the federated model training process. Without well-designed incentive, self-interested mobile devices will be unwilling to join federated learning tasks, which hinders the adoption of federated learning. To bridge this gap, in this paper, we adopt the contract theory to design an effective incentive mechanism for simulating the mobile devices with high-quality (i.e., high-accuracy) data to participate in federated learning. Numerical results demonstrate that the proposed mechanism is efficient for federated learning with improved learning accuracy.