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

TL;DR

This paper introduces a trust-based incentive mechanism for federated learning that evaluates participant reliability using trust scores, integrating blockchain for transparency, to promote honest contributions and system robustness.

Contribution

It presents a novel trust evaluation and incentive framework for federated learning, incorporating blockchain technology to enhance transparency and fairness.

Findings

Trust scores effectively differentiate reliable from unreliable nodes.

Blockchain integration automates trust management and incentives.

System improves robustness and fairness in federated learning.

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.