1 citations · 2 across the 7 of their papers we have counts for
8 papers
Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systems
Qi Rong Sua, Junhao Dong, Nguyen Duc Thai +3
Multi-agent trading systems built on large language models (LLMs) are beginning to appear in quantitative finance, yet their robustness to adversarial inputs is largely unknown. We…
Authority-Preserving Evaluation of Medical Vision-Language Assistants
Flint Xiaofeng Fan, Cheston Tan, Yew-Soon Ong +1
Medical vision-language models can propose how urgently a skin lesion should be reviewed, but the local service retains authority to accept or replace that proposal under referral…
FedPref: Federated Preference Learning for Structured Radiology Report Extraction
Flint Xiaofeng Fan, Cheston Tan, Yew-Soon Ong +1
Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schema. Learning this extraction requires labe…
FedHPD: Heterogeneous Federated Reinforcement Learning via Policy Distillation
Wenzheng Jiang, Ji Wang, Xiongtao Zhang +3
Federated Reinforcement Learning (FedRL) improves sample efficiency while preserving privacy; however, most existing studies assume homogeneous agents, limiting its applicability i…
FedRLHF: A Convergence-Guaranteed Federated Framework for Privacy-Preserving and Personalized RLHF
Flint Xiaofeng Fan, Cheston Tan, Yew-Soon Ong +2
In the era of increasing privacy concerns and demand for personalized experiences, traditional Reinforcement Learning with Human Feedback (RLHF) frameworks face significant challen…
CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening
Hei Yi Mak, Flint Xiaofeng Fan, Luca A. Lanzendörfer +3
In this study, we delve into Federated Reinforcement Learning (FedRL) in the context of value-based agents operating across diverse Markov Decision Processes (MDPs). Existing FedRL…
FedHQL: Federated Heterogeneous Q-Learning
Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai +3
Federated Reinforcement Learning (FedRL) encourages distributed agents to learn collectively from each other's experience to improve their performance without exchanging their raw…
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai +3
The growing literature of Federated Learning (FL) has recently inspired Federated Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better decision-…