Blockchain-Based Decentralized Federated Learning for Secure AI Model Training
DOI:
https://doi.org/10.22399/ijcesen.2487Keywords:
Blockchain, Decentralized Federated Learning, AI Model Training, Data Privacy, Security, Smart ContractsAbstract
With the rapid growth of Artificial Intelligence (AI) and machine learning models, the demand for large-scale data and computing resources has surged. However, this centralized approach to training AI models raises significant concerns about data privacy, security, and resource management. In this paper, we propose a Blockchain-Based Decentralized Federated Learning (BC-DFL) framework to address these challenges while ensuring privacy, security, and fairness in AI model training. The BC-DFL framework leverages blockchain technology to create a decentralized, transparent, and secure environment for collaborative AI model training, where data remains on local devices, and only model updates are shared. Federated Learning Integration: A decentralized approach to training machine learning models that preserves data privacy by ensuring that data never leaves the local device.Blockchain for Security and Transparency: Blockchain is used to securely aggregate model updates, verify authenticity, and ensure transparency in the training process. Smart contracts are employed to enforce privacy policies and incentivize participants.Decentralization: Unlike traditional centralized systems, BC-DFL eliminates the need for a central server, distributing both computational load and model training across multiple nodes. We evaluate the performance of BC-DFL in comparison with traditional centralized federated learning frameworks. Our experiments, conducted on a set of benchmark datasets, demonstrate that BC-DFL achieves 85% model accuracy, with 20% improved privacy due to decentralized training. Moreover, it ensures 100% traceability of model updates and maintains near-zero data leakage between participating nodes.This work demonstrates the potential of combining blockchain with federated learning to develop secure, efficient, and scalable AI models, suitable for environments where privacy and data security are paramount..
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