4 papers
When Secure Aggregation Falls Short: Achieving Long-Term Privacy in Asynchronous Federated Learning for LEO Satellite Networks
Mohamed Elmahallawy, Tie Luo
Secure aggregation is a common technique in federated learning (FL) for protecting data privacy from both curious internal entities (clients or server) and external adversaries (ea…
Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation
Xiaohui Chen, Tie Luo
In the field of Medical Imaging, extensive research has been dedicated to leveraging its potential in uncovering critical diagnostic features in patients. Artificial Intelligence (…
Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDM
Mohamed Elmahallawy, Tie Luo, Khaled Ramadan
Space AI has become increasingly important and sometimes even necessary for government, businesses, and society. An active research topic under this mission is integrating federate…
Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning
Mohamed Elmahallawy, Tie Luo
In the ambitious realm of space AI, the integration of federated learning (FL) with low Earth orbit (LEO) satellite constellations holds immense promise. However, many challenges p…