12 citations · 98 across the 61 of their papers we have counts for
15 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…
Block-As-Domain Adaptation for Workload Prediction from fNIRS Data
Jiyang Wang, Ayse Altay, Senem Velipasalar
Functional near-infrared spectroscopy (fNIRS) is a non-intrusive way to measure cortical hemodynamic activity. Predicting cognitive workload from fNIRS data has taken on a diffuse…
Anomaly Detection via Learning-Based Sequential Controlled Sensing
Geethu Joseph, Chen Zhong, M. Cenk Gursoy +2
In this paper, we address the problem of detecting anomalies among a given set of binary processes via learning-based controlled sensing. Each process is parameterized by a binary…
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…
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…