Not stated in source
2026 Needs review
Model Protection and Privacy Preservation of Federated Learning using Homomorphic Encryption
Abstract
Modern Machine Learning (ML) faces many critical challenges, including fragmented data silos and increasingly strict data privacy regulations. Applications in delicate fields like healthcare and finance are particularly impacted since access to extensive datasets is restricted by stringent legal and ethical requirements. Sensitive data, such as pregnancy-related medical records, particularly those involving termination, carries high risks of stigma, domestic violence, and social repercussions if exposed. Therefore, secure and privacy-preserving ML methods are essential for enabling collaborative learning without compromising individual privacy.
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