Enhancing Privacy and Security in Federated Learning Through Blockchain-based Decentralized Trust Models

Federated learning (FL) is a distributed machine learning paradigm that enables collaborative model training across decentralized datasets, preserving data privacy. However, the lack of mechanisms to ensure trust and security in federated learning systems remains a significant challenge. This paper proposes a blockchain-based decentralized framework designed to address these issues by incorporating blockchain technology to establish a secure, transparent, and trustworthy federated learning environment. The framework leverages a consortium blockchain to facilitate model aggregation, reputation assessment, and reward distribution through smart contracts and consensus protocols. Data owners, as federated learning participants, retain control over their local datasets, training local models and sharing gradients for global model updates. The proposed system ensures transparency and integrity by recording training results, reputation scores, and rewards on an immutable blockchain ledger. Furthermore, the paper details the design of several smart contracts to manage federated learning tasks, participant selection, and incentive mechanisms, thereby promoting fairness and motivating high-quality contributions. A parameter-aggregated blockchain structure, incorporating Merkle trees and proof-of-work (PoW), ensures the consistency and security of the shared model data. The results demonstrate that the proposed framework provides a robust solution for decentralized, trustworthy federated learning, offering significant potential for privacy-preserving applications in various fields.