67 citations · 67 across the 2 of their papers we have counts for
8 papers · 1 filter
Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions
Sadia Qureshi, Thanveer Shaik, Xiaohui Tao +4
The growing demand for data privacy in Machine Learning (ML) applications has seen Machine Unlearning (MU) emerge as a critical area of research. As the `right to be forgotten' bec…
Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Federated Learning (FL) is currently one of the most popular technologies in the field of Artificial Intelligence (AI) due to its collaborative learning and ability to preserve cli…
PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Reinforcement learning has been increasingly applied in monitoring applications because of its ability to learn from previous experiences and can make adaptive decisions. However,…
Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Effective patient monitoring is vital for timely interventions and improved healthcare outcomes. Traditional monitoring systems often struggle to handle complex, dynamic environmen…
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Machine Unlearning is an emerging field that addresses data privacy issues by enabling the removal of private or irrelevant data from the Machine Learning process. Challenges relat…
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence
Thanveer Shaik, Xiaohui Tao, Haoran Xie +3
Reinforcement learning is well known for its ability to model sequential tasks and learn latent data patterns adaptively. Deep learning models have been widely explored and adopted…