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This paper introduces a trust-based incentive framework for semi-decentralized federated learning that uses dynamic scoring to reward high-quality contributions while penalizing malicious nodes via smart contracts. By integrating blockchain automation, the system aims to enhance model integrity without compromising data privacy during collaborative training across multiple participants.
Trust-Based Incentive Mechanisms in Semi-Decentralized Federated Learning Systems
arXiv:2602.08290v2 Announce Type: replace Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
arXiv:2602.08290 [cs.LG]
(or arXiv:2602.08290v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.08290 Focus to learn more arXiv-issued DOI via DataCite
Lecture Notes in Networks and Systems, vol 1920. Springer, Cham
https://doi.org/10.1007/978-3-032-22088-2_16 Focus to learn more DOI(s) linking to related resources
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