Federated Learning and Privacy-Preserving Search
Abstract
Academic libraries are rapidly evolving into intelligent, user-centric platforms in the digital era. However, personalized resource discovery often comes at the cost of user privacy in centralized systems. This chapter explores the integration of Federated Learning (FL) and privacy-preserving technologies such as Differential Privacy and Secure Multi-Party Computation to address this challenge. FL enables decentralized model training, ensuring that sensitive user data remains local while still supporting personalization and collaborative model improvement across institutions. Through real-world case studies, the chapter demonstrates how academic libraries can deliver secure, ethical, and scalable content recommendation systems. The proposed framework fosters institutional trust, compliance with data regulations, and intelligent discovery without compromising privacy.
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