Secure Federated Learning Algorithms for Vertical and Combined Data Partitioning

With the growing need for collaborative machine learning across institutions holding sensitive data, ensuring data privacy without compromising model performance has become an important challenge. This work introduces secure federated learning algorithms that use encryption and masking techniques to...

Popoln opis

Shranjeno v:

Bibliografske podrobnosti
Principais autores:	Amir Anees, Ding Ming, Gnana Bharathy, Lois Holloway
Format:	Artigo
Jezik:	Inglês
Izdano:	MDPI AG 2026-06-01
Serija:	Future Internet
Teme:	federated learning vertical data partitioning combined data partitioning gradient inversion attack data privacy secure data sharing
Online dostop:	https://www.mdpi.com/1999-5903/18/6/302
Oznake:	Označite Brez oznak, prvi označite!

Secure Federated Learning Algorithms for Vertical and Combined Data Partitioning

Podobne knjige/članki