Amir Anees
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Amir Anees has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Gnana Bharathy
School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
1 shared publication - Lois Holloway
South Western Sydney Clinical School, Faculty of Medicine, UNSW, Sydney, Australia
1 shared publication - Ming Ding
CSIRO
1 shared publication - Muthuvel Arumugam
ISRO
1 shared publication P Gokulan
Associate Professor, Sri Vijay Vidyalaya College Of Pharmacy, Dharmapuri, Tamilnadu, India
1 shared publicationS R Aravind
1 shared publication
Latest Publications
- Open Access
- 3 June 2026
- Published by MDPI AG in Future Internet
- Vol. 18 (6) , 302
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 protect the privacy of data during collaborative model training. Three federated learning algorithms were developed: one for vertical federated learning and two combining horizontal and vertical data partitioning. The proposed algorithms are designed such that participating clients communicate only with the server, even when data exchange between clients is required. This exchange occurs through the server with the help of encryption and masking. The performance of the algorithms, evaluated in terms of accuracy and loss, shows competitive results. The accuracy remains unchanged compared to the centralised scenario for the vertical federated learning algorithm and one of the combined federated learning algorithms, and it remains highly competitive with the other combined federated learning algorithm. The privacy analyses conducted as part of this work demonstrate no risk of data leakage ensuring that no party involved can infer sensitive information.
- 3 June 2026
- Published by MDPI AG in Future Internet
- Vol. 18 (6) , 302
Open AccessSecure 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 protect the privacy of data during collaborative model training. Three federated learning algorithms were developed: one for vertical federated learning and two combining horizontal and vertical data partitioning. The proposed algorithms are designed such that participating clients communicate only with the server, even when data exchange between clients is required. This exchange occurs through the server with the help of encryption and masking. The performance of the algorithms, evaluated in terms of accuracy and loss, shows competitive results. The accuracy remains unchanged compared to the centralised scenario for the vertical federated learning algorithm and one of the combined federated learning algorithms, and it remains highly competitive with the other combined federated learning algorithm. The privacy analyses conducted as part of this work demonstrate no risk of data leakage ensuring that no party involved can infer sensitive information.