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Enhancing Federated Learning with Homomorphic Encryption and Multi-Party Computation for improved privacy

Year: 2024

Type: conference-paper

Source: IEEE Future Networks World Forum (FNWF)

DOI: 10.1109/fnwf63303.2024.11028749

Authors: Pedro Tomás, Samira Kamali Poorazad, Chafika Benzaïd, Luis Rosa, Jorge Proença, Tarik Taleb, Luis Cordeiro

Institutions: OneSource (Portugal), University of Oulu, Ruhr University Bochum

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

OpenAlex ID: W4411232758

FWCI: 0.317

Cites: 17

Cited by: 1

Related to: 10