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