A5095771096- Privacy-Preserving Technologies in Data
- Vehicular Ad Hoc Networks (VANETs)
- Adversarial Robustness in Machine Learning
- Mobile Crowdsensing and Crowdsourcing
- Ethics and Social Impacts of AI
Virginia Tech
2023-2024
Deep Learning (DL) techniques are being used in various critical applications like self-driving cars. DL such as Neural Networks (DNN), Reinforcement (DRL), Federated (FL), and Transfer (TL) prone to adversarial attacks, which can make the perform poorly. Developing attacks their countermeasures is prerequisite for making artificial intelligence robust, secure, deployable. Previous survey papers only focused on one or two outdated. They do not discuss application domains, datasets, testbeds...
Federated Learning (FL) has emerged as a powerful approach that enables collaborative distributed model training without the need for data sharing. However, FL grapples with inherent heterogeneity challenges leading to issues such stragglers, dropouts, and performance variations. Selection of clients run an instance is crucial, but existing strategies introduce biases participation do not consider resource efficiency. Communication acceleration solutions proposed increase client also fall...