Hybrid Agile Method for Implementing Blockchain-Based Federated Learning in AI-Powered Universities

Abstract

Universities are increasingly becoming AI powered organizations that rely on data driven decision making through the large scale processing and exchange of data. Compliance with data protection regulations, particularly the GDPR, means that the use of AI creates significant privacy and regulatory risks. To enable universities, regulators, and employers to jointly learn from data using AI without violating data protection requirements, this paper proposes a privacy compliant permissioned consortium blockchain architecture with on chain/off chain data segmentation and federated learning. To ensure practical deployment in highly regulated higher education environments, the paper develops a Hybrid Agile implementation methodology. This methodology combines a Waterfall style prior assessment phase embedding legal, governance, and architectural constraints, followed by an Agile delivery phase governed by compliance gates. Together, the blueprint integrates regulatory compliance, technical architecture, and project management into a unified operational framework.