# Joint Service Pricing and Cooperative Relay Communication for Federated Learning

arXiv: https://arxiv.org/abs/1811.12082 · HTML: https://mlsanity.com/paper/1811.12082/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 22 of 22)

- [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)
- [Ten Challenging Problems in Federated Foundation Models](https://mlsanity.com/paper/2502.12176/) (2025)
- [Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness](https://mlsanity.com/paper/2108.03980/) (2021)
- [A Contract Theory based Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2108.05568/) (2021)
- [Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks](https://mlsanity.com/paper/2101.03627/) (2021)
- [Relay-Assisted Cooperative Federated Learning](https://mlsanity.com/paper/2107.09518/) (2021)
- [A Survey of Federated Evaluation in Federated Learning](https://mlsanity.com/paper/2305.08070/) (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)
- [Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications](https://mlsanity.com/paper/2310.05269/) (2023)
- [Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services](https://mlsanity.com/paper/2106.09261/) (2021)
- [Hierarchical Federated Learning Incentivization for Gas Usage Estimation](https://mlsanity.com/paper/2307.00233/) (2023)
- [An Auction-based Marketplace for Model Trading in Federated Learning](https://mlsanity.com/paper/2402.01802/) (2024)
- [Privacy-Aware Data Acquisition under Data Similarity in Regression Markets](https://mlsanity.com/paper/2312.02611/) (2023)
- [Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction](https://mlsanity.com/paper/2202.05139/) (2022)
- [Distributed Stochastic Gradient Descent with Cost-Sensitive and Strategic Agents](https://mlsanity.com/paper/2212.02049/) (2022)
- [Strategic Incentivization for Locally Differentially Private Federated Learning](https://mlsanity.com/paper/2508.07138/) (2025)
- [Knowledge-Free Correlated Agreement for Incentivizing Federated Learning](https://mlsanity.com/paper/2605.04747/) (2026)

## Abstract (arXiv)

For the sake of protecting data privacy and due to the rapid development of mobile devices, e.g., powerful central processing unit (CPU) and nascent neural processing unit (NPU), collaborative machine learning on mobile devices, e.g., federated learning, has been envisioned as a new AI approach with broad application prospects. However, the learning process of the existing federated learning platforms rely on the direct communication between the model owner, e.g., central cloud or edge server, and the mobile devices for transferring the model update. Such a direct communication may be energy inefficient or even unavailable in mobile environments. In this paper, we consider adopting the relay network to construct a cooperative communication platform for supporting model update transfer and trading. In the system, the mobile devices generate model updates based on their training data. The model updates are then forwarded to the model owner through the cooperative relay network. The model owner enjoys the learning service provided by the mobile devices. In return, the mobile devices charge the model owner certain prices. Due to the coupled interference of wireless transmission among the mobile devices that use the same relay node, the rational mobile devices have to choose their relay nodes as well as deciding on their transmission powers. Thus, we formulate a Stackelberg game model to investigate the interaction among the mobile devices and that between the mobile devices and the model owner. The Stackelberg equilibrium is investigated by capitalizing on the exterior point method. Moreover, we provide a series of insightful analytical and numerical results on the equilibrium of the Stackelberg game.