8 citations · 10 across the 10 of their papers we have counts for
10 papers
Structure-Adaptive Topology Optimization Framework for Photonic Band Gaps with TE-Polarized Sources
Sukhad Dnyanesh Joshi, Aditya Bahulikar, Feng Wang +2
Leveraging our structure-adaptive topology optimization framework based on the integration of the photonic density of states over a frequency window for the TM polarization of ligh…
Structure-Adaptive Topology Optimization Framework for Photonic Band Gaps with TM-Polarized Sources
Aditya Bahulikar, Feng Wang, Mustafa Cenk Gursoy +1
We present a structure-adaptive topology optimization framework for engineering photonic band gaps with TM-polarized sources based on computation of the photonic density of states…
Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy
Feng Wang, M. Cenk Gursoy, Senem Velipasalar
In this paper, we propose feature-based federated transfer learning as a novel approach to improve communication efficiency by reducing the uplink payload by multiple orders of mag…
Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies
Feng Wang, M. Cenk Gursoy, Senem Velipasalar
In this paper, we present a multi-agent deep reinforcement learning (deep RL) framework for network slicing in a dynamic environment with multiple base stations and multiple users.…
Maximum Knowledge Orthogonality Reconstruction with Gradients in Federated Learning
Feng Wang, Senem Velipasalar, M. Cenk Gursoy
Federated learning (FL) aims at keeping client data local to preserve privacy. Instead of gathering the data itself, the server only collects aggregated gradient updates from clien…
Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks
Wenbin Zhai, Liang Liu, Feng Wang +3
Existing FL-based approaches are based on the unrealistic assumption that the data on the client-side is fully annotated with ground truths. Furthermore, it is a great challenge ho…
Action Pick-up in Dynamic Action Space Reinforcement Learning
Jiaqi Ye, Xiaodong Li, Pangjing Wu +1
Most reinforcement learning algorithms are based on a key assumption that Markov decision processes (MDPs) are stationary. However, non-stationary MDPs with dynamic action space ar…
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning
Feng Wang, M. Cenk Gursoy, Senem Velipasalar
Federated learning has attracted growing interest as it preserves the clients' privacy. As a variant of federated learning, federated transfer learning utilizes the knowledge from…
Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control
Feng Wang, M. Cenk Gursoy, Senem Velipasalar
Deep reinforcement learning (DRL) has recently been used to perform efficient resource allocation in wireless communications. In this paper, the vulnerabilities of such DRL agents…
Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning
Feng Wang, Chen Zhong, M. Cenk Gursoy +1
As the applications of deep reinforcement learning (DRL) in wireless communications grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has…