A security-friendly privacy-preserving solution for federated learning

Author:

Karakoç FerhatORCID,Karaçay Leyli,Çomak De Cnudde Pinar,Gülen Utku,Fuladi Ramin,Soykan Elif Ustundag

Publisher

Elsevier BV

Subject

Computer Networks and Communications

Reference33 articles.

1. H.B. McMahan, E. Moore, D. Ramage, S. Hampson, B.A. y Arcas, Communication-Efficient Learning of Deep Networks from Decentralized Data, in: AISTATS, 2017.

2. FEDGAN-IDS: Privacy-preserving IDS using GAN and federated learning;Tabassum;Comput. Commun.,2022

3. Federated learning for intrusion detection system: Concepts, challenges and future directions;Agrawal;Comput. Commun.,2022

4. An ensemble deep federated learning cyber-threat hunting model for industrial internet of things;Jahromi;Comput. Commun.,2023

5. A survey on security and privacy of federated learning;Mothukuri;Future Gener. Comput. Syst.,2021

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