# Trust-Based Incentive Mechanisms in Semi-Decentralized Federated Learning Systems

arXiv: https://arxiv.org/abs/2602.08290 · HTML: https://mlsanity.com/paper/2602.08290/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 9 of 9)

- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape](https://mlsanity.com/paper/2405.03636/) (2024)
- [Blockchain-based Federated Learning with SMPC Model Verification Against Poisoning Attack for Healthcare Systems](https://mlsanity.com/paper/2304.13360/) (2023)
- [Blockchain for Large Language Model Security and Safety: A Holistic Survey](https://mlsanity.com/paper/2407.20181/) (2024)
- [Privacy-Preserving in Blockchain-based Federated Learning Systems](https://mlsanity.com/paper/2401.03552/) (2024)
- [FederatedTrust: A Solution for Trustworthy Federated Learning](https://mlsanity.com/paper/2302.09844/) (2023)
- [Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior](https://mlsanity.com/paper/2307.10492/) (2023)
- [Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality](https://mlsanity.com/paper/2310.19287/) (2023)
- [Trust and Resilience in Federated Learning Through Smart Contracts Enabled Decentralized Systems](https://mlsanity.com/paper/2407.06862/) (2024)

## Abstract (arXiv)

In federated learning (FL), decentralized model training allows multi-ple participants to collaboratively improve a shared machine learning model without exchanging raw data. However, ensuring the integrity and reliability of the system is challenging due to the presence of potentially malicious or faulty nodes that can degrade the model's performance. This paper proposes a novel trust-based incentive mechanism designed to evaluate and reward the quality of contributions in FL systems. By dynamically assessing trust scores based on fac-tors such as data quality, model accuracy, consistency, and contribution fre-quency, the system encourages honest participation and penalizes unreliable or malicious behavior. These trust scores form the basis of an incentive mechanism that rewards high-trust nodes with greater participation opportunities and penal-ties for low-trust participants. We further explore the integration of blockchain technology and smart contracts to automate the trust evaluation and incentive distribution processes, ensuring transparency and decentralization. Our proposed theoretical framework aims to create a more robust, fair, and transparent FL eco-system, reducing the risks posed by untrustworthy participants.