BLEND blockchain and federated learning enabled data sharing network.

Area of Science:

• Decentralized Systems
• Machine Learning Security
• Data Privacy

Background:

• Blockchain and federated learning offer secure, privacy-preserving data sharing and collaborative model training.
• Existing methods face scalability issues, computational overhead, and privacy challenges in dynamic environments (IoT, healthcare, smart cities).
• Traditional blockchain solutions incur high computational costs for transaction validation and consensus, while federated learning struggles with efficient, secure aggregation.

Purpose of the Study:

• To propose BLEND, a Blockchain- and federated-learning-enabled network for Data sharing, addressing the limitations of current decentralized approaches.
• To enhance security, scalability, and operational efficiency in decentralized machine learning.
• To enable privacy-friendly, large-scale distributed data sharing and model training.

Main Methods:

• Integration of a novel consensus protocol for improved efficiency.
• Implementation of adaptive encryption schemes that adjust in real-time based on threat intelligence.
• Utilization of smart contract-based aggregation for secure and efficient model updates.

Main Results:

• BLEND demonstrates superior performance over existing blockchain-based federated learning methods in latency, computation cost, and model accuracy.
• The framework reduces computational overhead by up to 20% while maintaining 90-95% of the original model's accuracy.
• Achieves lower latency compared to traditional methods, enabling practical, privacy-friendly scenarios.

Conclusions:

• BLEND provides a performant, robust, and scalable solution for decentralized data sharing and collaborative model training.
• The framework effectively balances security, privacy, and performance in applications like healthcare and IoT.
• BLEND overcomes the limitations of traditional methods, offering a significant advancement in decentralized machine learning ecosystems.