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Potentials Chair Editor-In-Chief
Mohammad Faizal Ahmad Fauzi
faizal.fauzi@kpju.edu.my

Managing Editor
AndreAnna McLean – IEEE
a.r.mclean@ieee.org

Student Editor
Moira Prates
moira@ieee.org

Associate Editor
Azfar Adib
adib_eee@yahoo.com

Associate Editor
Shaikh Fattah
s.a.fattah@ieee.org

Associate Editor
Bee Theng Lau
blau@swinburne.edu.my

Associate Editor
Dr. Prashant Nair
prashant@amrita.edu

Associate Editor
Supavadee Aramvith
Supavadee.A@chula.ac.th

Student Activities Committee Chair
Subodha Charles
s.charles@ieee.org

Corresponding Editor
Dario Schor
schor@ieee.org

Corresponding Editor
Stamatis Dragoumanos
sdragou@gmail.com

Corresponding Editor
John Benedict Boggala
john.benedict@ieee.org

Corresponding Editor
Syrine Ferjaoui

Corresponding Editor
Kapal Dev
kapal.dev@ieee.org

Corresponding Editor
Deepak Puthal
deepak.puthal@ieee.org

Corresponding Editor
Saraju Mohanty

Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

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