collaborators

7 papers

cs.LG2025

Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles

Ferdous Pervej, Richeng Jin, Md Moin Uddin Chowdhury +3

Privacy-preserving distributed machine learning (ML) and aerial connected vehicle (ACV)-assisted edge computing have drawn significant attention lately. Since the onboard sensors o…

cs.LG2025

Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival

Ferdous Pervej, Minseok Choi, Andreas F. Molisch

Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distribu…

cs.NI2025

Revenue Optimization in Wireless Video Caching Networks: A Privacy-Preserving Two-Stage Solution

Yijing Zhang, Md-Ferdous Pervej, Andreas F. Molisch

Video caching can significantly improve delivery efficiency and enhance quality of video streaming, which constitutes the majority of wireless communication traffic. Due to limited…

cs.NI2025

Revenue Optimization in Video Caching Networks with Privacy-Preserving Demand Predictions

Yijing Zhang, Ferdous Pervej, Andreas F. Molisch

Performance of video streaming, which accounts for most of the traffic in wireless communication, can be significantly improved by caching popular videos at the wireless edge. Dete…

eess.SP2025

Double Directional Wireless Channel Generation: A Statistics-Informed Generative Approach

Md-Ferdous Pervej, Patel Pratik, Koushik Manjunatha +2

Channel models that represent various operating conditions a communication system might experience are important for design and standardization of any communication system. While s…

cs.LG2025

Personalized Hierarchical Split Federated Learning in Wireless Networks

Md-Ferdous Pervej, Andreas F. Molisch

Extreme resource constraints make large-scale machine learning (ML) with distributed clients challenging in wireless networks. On the one hand, large-scale ML requires massive info…