Protecting Collaborative Education: A Blockchain-Powered Trust and Reward System to Reduce Poisoning Attacks in Federated Learning

Federated Learning (FL) is a game-changing idea in education that enables institutions to create robust AI models for individualized learning while maintaining the highest level of privacy for student data. However, due to its decentralized nature, Federated Education is very vulnerable to Poisoning Attacks (data and model poisoning), where nodes are compromised to provide false information to the global model, causing a substantial decline in its accuracy. This research aims to introduce a special Blockchain-based Trust Management and Incentive Framework for Federated Education to address this critical security threat. Our proposed design is a dynamic and decentralized system of reputation, facilitated by self-executing Smart Contracts and cryptographic validation. Real-time trust scores are generated based on an assessment of the dependability of each participating node in the past, in addition to cryptographic validation of learning. Although malevolent nodes that launch poisoning attacks are easily identified, penalized, and excluded from the aggregation, honest and contributing institutions with high-quality and localized updates to models are algorithmically rewarded with tokenized incentives. Based on preliminary simulations, our blockchain-based system is able to counter targeted poisoning attacks, with a global model accuracy of over 95% maintained even with up to 30% compromised nodes, while traditional FL models see a considerable reduction in accuracy (up to 30-40%) with 15% malicious nodes. This proposed system for the future of AI in education is not only able to counter negative threats but is also in a position to foster a system that is transparent, sustainable, and highly collaborative.