A Decentralized and Efficient Crowdfunding Framework for Secure Transactions and User Engagement

Abstract

Crowdfunding has become essential for financing entrepreneurial projects, innovative projects, and social initiatives. However, existing platforms face critical challenges, including a lack of transparency, low user engagement, data privacy concerns, and ineffective personalization of user experiences. To address these limitations, this study introduces a novel decentralized crowdfunding framework that integrates Federated Learning (FL), blockchain technology, and Q-learning to enhance security, transparency, and user engagement. The framework leverages FL to collaboratively train models across distributed datasets while ensuring privacy preservation by eliminating the need to share raw user data. Blockchain technology is utilized to ensure tamper-proof transaction records and automate trustless interactions through smart contracts, effectively preventing fraud while increasing transparency. Additionally, a Q-learning-based incentive mechanism is incorporated to predict and stimulate user engagement, ensuring dynamic long-term engagement. The experimental evaluation illustrates that the designed framework attains state-of-the-art performance with an accuracy rate of 99.39%, surpassing existing methodologies. The results demonstrate the effectiveness of the framework in providing a secure, decentralized, and highly personalized crowdfunding system, raising trust and engagement among stakeholders and resolving long-standing issues in crowdfunding platforms.