5 citations · 9 across the 10 of their papers we have counts for
10 papers
COOP: Defining, Observing, and Repairing Cooperation in LLM Multi-Agent Systems
Hanqing Yang, Narjes Nourzad, Shiyu Chen +3
Many complex tasks require extended effort, diverse capabilities, or coordinated actions beyond what a single agent can provide. However, simply adding more agents does not guarant…
Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning
Haoran Zhang, Zejun Gong, Zekai Li +3
Federated learning (FL) allows edge devices to collaboratively train models without sharing local data. As FL gains popularity, clients may need to train multiple unrelated FL mode…
LLM-Powered Decentralized Generative Agents with Adaptive Hierarchical Knowledge Graph for Cooperative Planning
Hanqing Yang, Jingdi Chen, Marie Siew +2
Developing intelligent agents for long-term cooperation in dynamic open-world scenarios is a major challenge in multi-agent systems. Traditional Multi-agent Reinforcement Learning…
Fair Concurrent Training of Multiple Models in Federated Learning
Marie Siew, Haoran Zhang, Jong-Ik Park +6
Federated learning (FL) enables collaborative learning across multiple clients. In most FL work, all clients train a single learning task. However, the recent proliferation of FL a…
An LLM-Based Digital Twin for Optimizing Human-in-the Loop Systems
Hanqing Yang, Marie Siew, Carlee Joe-Wong
The increasing prevalence of Cyber-Physical Systems and the Internet of Things (CPS-IoT) applications and Foundation Models are enabling new applications that leverage real-time co…
Differentially Private Deep Q-Learning for Pattern Privacy Preservation in MEC Offloading
Shuying Gan, Marie Siew, Chao Xu +1
Mobile edge computing (MEC) is a promising paradigm to meet the quality of service (QoS) requirements of latency-sensitive IoT applications. However, attackers may eavesdrop on the…
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations
Marie Siew, Shikhar Sharma, Zekai Li +5
In edge computing, users' service profiles are migrated due to user mobility. Reinforcement learning (RL) frameworks have been proposed to do so, often trained on simulated data. H…
Towards Effective Resource Procurement in MEC: a Resource Re-selling Framework
Marie Siew, Shikhar Sharma, Kun Guo +4
On-demand and resource reservation pricing models have been widely used in cloud computing, catering to different user requirements. Nevertheless, in Multi-Access Edge Computing (M…
Let's Share VMs: Optimal Placement and Pricing across Base Stations in MEC Systems
Marie Siew, Kun Guo, Desmond Cai +2
In mobile edge computing (MEC) systems, users offload computationally intensive tasks to edge servers at base stations. However, with unequal demand across the network, there might…
Optimal Pricing for Job Offloading in the MEC System with Two Priority Classes
Lingxiang Li, Marie Siew, Tony Q. S. Quek +1
Multi-Access edge computing (MEC) is an emerging paradigm where users offload computationally intensive jobs to the Access Point (AP). Given that the AP's resources are shared by s…