# How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?

arXiv: https://arxiv.org/abs/2412.07813 · HTML: https://mlsanity.com/paper/2412.07813/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 20 of 20)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks](https://mlsanity.com/paper/2303.15991/) (2023)
- [Detailed comparison of communication efficiency of split learning and federated learning](https://mlsanity.com/paper/1909.09145/) (2019)
- [Split Learning over Wireless Networks: Parallel Design and Resource Management](https://mlsanity.com/paper/2204.08119/) (2022)
- [HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning](https://mlsanity.com/paper/2002.11343/) (2020)
- [Decentralized Federated Learning through Proxy Model Sharing](https://mlsanity.com/paper/2111.11343/) (2021)
- [Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning](https://mlsanity.com/paper/2009.05604/) (2020)
- [ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices](https://mlsanity.com/paper/2402.15903/) (2024)
- [Communication and Storage Efficient Federated Split Learning](https://mlsanity.com/paper/2302.05599/) (2023)
- [Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning](https://mlsanity.com/paper/2311.09441/) (2023)
- [Learning Structure and Strength of CNN Filters for Small Sample Size Training](https://mlsanity.com/paper/1803.11405/) (2018)
- [Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions](https://mlsanity.com/paper/2309.09086/) (2023)
- [Multiple Classification with Split Learning](https://mlsanity.com/paper/2008.09874/) (2020)
- [Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation](https://mlsanity.com/paper/2208.07237/) (2022)
- [Market Model and Optimal Pricing Scheme of Big Data and Internet of Things (IoT)](https://mlsanity.com/paper/1602.03202/) (2016)
- [When MiniBatch SGD Meets SplitFed Learning:Convergence Analysis and Performance Evaluation](https://mlsanity.com/paper/2308.11953/) (2023)
- [QI-DPFL: Quality-Aware and Incentive-Boosted Federated Learning with Differential Privacy](https://mlsanity.com/paper/2404.08261/) (2024)

## Abstract (arXiv)

To alleviate the training burden in federated learning while enhancing convergence speed, Split Federated Learning (SFL) has emerged as a promising approach by combining the advantages of federated and split learning. However, despite its advantages, existing SFL studies have largely overlooked the strategic interactions among self-interested participants during the SFL process. In this framework, the SFL model owner can choose the cut layer to balance the training load between the server and clients, ensuring the necessary level of privacy for the clients. Additionally, the SFL model owner sets incentives to encourage client participation in the SFL process. The optimization strategies employed by the SFL model owner influence clients' decisions regarding the amount of data they contribute, taking into account the shared incentives over clients and anticipated energy consumption from both computation and networking during SFL. To address this framework, we model the problem using a hierarchical decision-making approach, formulated as a single-leader multi-follower Stackelberg game. We demonstrate the existence and uniqueness of the Nash equilibrium among clients and analyze the Stackelberg equilibrium by examining the leader's game. Furthermore, we discuss privacy concerns related to differential privacy and the criteria for selecting the minimum required cut layer. Our findings show that the Stackelberg equilibrium solution maximizes the utility for both the clients and the SFL model owner while achieving a well-balanced trade-off between model accuracy and the associated computing and networking overhead during the SFL process.