# Incentives for Federated Learning: a Hypothesis Elicitation Approach

arXiv: https://arxiv.org/abs/2007.10596 · HTML: https://mlsanity.com/paper/2007.10596/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [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)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [Data-Free Evaluation of User Contributions in Federated Learning](https://mlsanity.com/paper/2108.10623/) (2021)
- [Incentives in Private Collaborative Machine Learning](https://mlsanity.com/paper/2404.01676/) (2024)
- [Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring](https://mlsanity.com/paper/2506.08429/) (2025)
- [On the Effect of Defections in Federated Learning and How to Prevent Them](https://mlsanity.com/paper/2311.16459/) (2023)
- [Reward-Based 1-bit Compressed Federated Distillation on Blockchain](https://mlsanity.com/paper/2106.14265/) (2021)
- [Incentivizing High-quality Participation From Federated Learning Agents](https://mlsanity.com/paper/2506.16731/) (2025)
- [Knowledge-Free Correlated Agreement for Incentivizing Federated Learning](https://mlsanity.com/paper/2605.04747/) (2026)
- [Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning](https://mlsanity.com/paper/2605.11889/) (2026)

## Abstract (arXiv)

Federated learning provides a promising paradigm for collecting machine learning models from distributed data sources without compromising users' data privacy. The success of a credible federated learning system builds on the assumption that the decentralized and self-interested users will be willing to participate to contribute their local models in a trustworthy way. However, without proper incentives, users might simply opt out the contribution cycle, or will be mis-incentivized to contribute spam/false information. This paper introduces solutions to incentivize truthful reporting of a local, user-side machine learning model for federated learning. Our results build on the literature of information elicitation, but focus on the questions of eliciting hypothesis (rather than eliciting human predictions). We provide a scoring rule based framework that incentivizes truthful reporting of local hypotheses at a Bayesian Nash Equilibrium. We study the market implementation, accuracy as well as robustness properties of our proposed solution too. We verify the effectiveness of our methods using MNIST and CIFAR-10 datasets. Particularly we show that by reporting low-quality hypotheses, users will receive decreasing scores (rewards, or payments).