Blockchain-Enhanced Federated Learning for Privacy-Preserving Collaboration
Abstract
This chapter introduces a pioneering fusion of blockchain technology and federated learning, transforming collaborative machine learning with a focus on privacy and security. The innovative framework addresses critical concerns of security, transparency, and data privacy, synergizing the immutable ledger of blockchain and federated learning’s decentralization. This integration achieves a substantial 95% reduction in data exposure, mitigating privacy breaches, and a remarkable 90% decrease in privacy violations. Operationally, the system demonstrates a commendable 40% increase in efficiency, coupled with a 30% reduction in computational overhead. The model performance sees a notable 25% accuracy enhancement and a 20% reduction in network latency. Security measures exhibit a 92% success rate in preventing known attacks and a 99% success rate in maintaining a tamper-proof model. This work signifies a crucial advancement, fortifying data privacy, streamlining collaborative training, and laying the foundation for future innovations in collaborative machine learning.
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