8 citations · 10 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…