5 citations · 11 across the 5 of their papers we have counts for
5 papers
Split Learning without Local Weight Sharing to Enhance Client-side Data Privacy
Ngoc Duy Pham, Tran Khoa Phan, Alsharif Abuadbba +3
Split learning (SL) aims to protect user data privacy by distributing deep models between client-server and keeping private data locally. In SL training with multiple clients, the…
Adversarial Models Towards Data Availability and Integrity of Distributed State Estimation for Industrial IoT-Based Smart Grid
Haftu Tasew Reda, Abdun Mahmood, Adnan Anwar +1
Security issue of distributed state estimation (DSE) is an important prospect for the rapidly growing smart grid ecosystem. Any coordinated cyberattack targeting the distributed sy…
Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction
Ngoc Duy Pham, Alsharif Abuadbba, Yansong Gao +2
Split learning (SL) enables data privacy preservation by allowing clients to collaboratively train a deep learning model with the server without sharing raw data. However, SL still…
Can the Multi-Incoming Smart Meter Compressed Streams be Re-Compressed?
Sharif Abuadbba, Ayman Ibaida, Ibrahim Khalil +3
Smart meters have currently attracted attention because of their high efficiency and throughput performance. They transmit a massive volume of continuously collected waveform readi…
Heterogeneous Dynamic Priority Scheduling in time critical applications: Mobile Wireless Sensor Networks
Arvind Viswanathan, Dr. Garimella Rama Murthy
In the unlicensed band, the notion of primary user and secondary user (To implement cognitive radio) is not explicit. By dynamic priority assignment we propose to implement cogniti…