# An Exploratory Analysis on Users' Contributions in Federated Learning

arXiv: https://arxiv.org/abs/2011.06830 · HTML: https://mlsanity.com/paper/2011.06830/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 15 of 15)

- [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)
- [Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective](https://mlsanity.com/paper/2106.11814/) (2021)
- [Data-Free Evaluation of User Contributions in Federated Learning](https://mlsanity.com/paper/2108.10623/) (2021)
- [Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data](https://mlsanity.com/paper/2108.07927/) (2021)
- [Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning](https://mlsanity.com/paper/2306.05592/) (2023)
- [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023)
- [An Auction-based Marketplace for Model Trading in Federated Learning](https://mlsanity.com/paper/2402.01802/) (2024)
- [A Comprehensive Study of Shapley Value in Data Analytics](https://mlsanity.com/paper/2412.01460/) (2024)
- [Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks](https://mlsanity.com/paper/2303.10254/) (2023)
- [Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation](https://mlsanity.com/paper/2410.15620/) (2024)
- [Detect & Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning](https://mlsanity.com/paper/2506.23583/) (2025)
- [On the Fragility of Contribution Score Computation in Federated Learning](https://mlsanity.com/paper/2509.19921/) (2025)
- [FRIDA: Free-Rider Detection using Privacy Attacks](https://mlsanity.com/paper/2410.05020/) (2024)
- [Beyond performance-wise Contribution Evaluation in Federated Learning](https://mlsanity.com/paper/2602.22470/) (2026)

## Abstract (arXiv)

Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. Its core principle is to learn from large amount of users data while preserving data privacy by design as collaborative users only need to share the machine learning models and keep data locally. The main challenge for such systems is to provide incentives to users to contribute high-quality models trained from their local data. In this paper, we aim to answer how well incentives recognize (in)accurate local models from honest and malicious users, and perceive their impacts on the model accuracy of federated learning systems. We first present a thorough survey on two contrasting perspectives: incentive mechanisms to measure the contribution of local models by honest users, and malicious users to deliberately degrade the overall model. We conduct simulation experiments to empirically demonstrate if existing contribution measurement schemes can disclose low-quality models from malicious users. Our results show there exists a clear tradeoff among measurement schemes in terms of the computational efficiency and effectiveness to distill the impact of malicious participants. We conclude this paper by discussing the research directions to design resilient contribution incentives.