This research introduces a blockchain-based architecture that transforms federated learning by removing central aggregators and adding incentive mechanisms.

Key innovations:

• Eliminates single points of failure through decentralized validation and aggregation
• Implements token-based incentives to reward quality contributions from participants
• Enables scalable training of resource-intensive models like LLMs while preserving privacy
• Addresses trust issues inherent in traditional centralized federated learning systems

For security professionals, this framework offers a robust solution to data privacy challenges while maintaining model performance through distributed training—critical for organizations developing AI solutions with sensitive data.

Blockchain-based Framework for Scalable and Incentivized Federated Learning