1 citations · 1 across the 3 of their papers we have counts for
4 papers
Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing
Lyudong Jin, Ming Tang, Jiayu Pan +2
In the realm of emerging real-time networked applications such as cyber-physical systems (CPS), the Age of Information (AoI) has emerged as a pivotal metric for evaluating timeline…
Hessian-Free Online Certified Unlearning
Xinbao Qiao, Meng Zhang, Ming Tang +1
Machine unlearning strives to uphold the data owners' right to be forgotten by enabling models to selectively forget specific data. Recent advances suggest pre-computing and storin…
Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing
Lyudong Jin, Ming Tang, Meng Zhang +1
Mobile edge computing (MEC) is a promising paradigm for real-time applications with intensive computational needs (e.g., autonomous driving), as it can reduce the processing delay.…
Multi-objective Deep Reinforcement Learning for Mobile Edge Computing
Ning Yang, Junrui Wen, Meng Zhang +1
Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy consumption. Howev…