Decentralized Federated Learning: A Blockchain Approach to Secure Collaborative AI Training
Abstract
Federated learning (FL) enables collaborative AI model training across multiple decentralized devices while preserving data privacy. However, traditional FL approaches rely on a central server, which introduces risks related to data security and trust. This paper proposes a decentralized federated learning framework based on blockchain technology, eliminating the need for a central authority. By leveraging blockchain, we create a secure and transparent environment where model updates are recorded on an immutable ledger. Smart contracts are used to enforce fairness and consistency in model aggregation, ensuring that contributions from all participants are fairly integrated. Our approach also includes mechanisms to detect and penalize malicious behavior, such as model poisoning. Experimental results demonstrate that the decentralized FL framework achieves high levels of accuracy and robustness, making it a promising solution for privacy-preserving AI training in untrusted environments.
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References
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