# Estimation of Individual Device Contributions for Incentivizing Federated Learning

arXiv: https://arxiv.org/abs/2009.09371 · HTML: https://mlsanity.com/paper/2009.09371/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 14 of 14)

- [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)
- [Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review](https://mlsanity.com/paper/2205.07855/) (2022)
- [Towards Interpretable Federated Learning](https://mlsanity.com/paper/2302.13473/) (2023)
- [On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator](https://mlsanity.com/paper/2108.02712/) (2021)
- [Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks](https://mlsanity.com/paper/2502.04850/) (2025)
- [Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement](https://mlsanity.com/paper/2201.02410/) (2022)
- [Exploring Data Redundancy in Real-world Image Classification through Data Selection](https://mlsanity.com/paper/2306.14113/) (2023)
- [Reward-Based 1-bit Compressed Federated Distillation on Blockchain](https://mlsanity.com/paper/2106.14265/) (2021)
- [Hierarchical Federated Learning Incentivization for Gas Usage Estimation](https://mlsanity.com/paper/2307.00233/) (2023)
- [Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation](https://mlsanity.com/paper/2410.15620/) (2024)
- [History-Aware and Dynamic Client Contribution in Federated Learning](https://mlsanity.com/paper/2403.07151/) (2024)

## Abstract (arXiv)

Federated learning (FL) is an emerging technique used to train a machine-learning model collaboratively using the data and computation resource of the mobile devices without exposing privacy-sensitive user data. Appropriate incentive mechanisms that motivate the data and mobile-device owner to participate in FL is key to building a sustainable platform for FL. However, it is difficult to evaluate the contribution level of the devices/owners to determine appropriate rewards without large computation and communication overhead. This paper proposes a computation-and communication-efficient method of estimating a participating device's contribution level. The proposed method enables such estimation during a single FL training process, there by reducing the need for traffic and computation overhead. The performance evaluations using the MNIST dataset show that the proposed method estimates individual participants' contributions accurately with 46-49% less computation overhead and no communication overhead than a naive estimation method.