Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. - Flint Xiaofeng Fan; Yining Ma 001; Zhongxiang Dai; Wei Jing; Cheston Tan; Bryan Kian Hsiang Low (NeurIPS, 2021)

Federated Neural Bandit. - Zhongxiang Dai; Yao Shu; Arun Verma; Flint Xiaofeng Fan; Bryan Kian Hsiang Low; Patrick Jaillet (CoRR, 2022)

FedHQL: Federated Heterogeneous Q-Learning. - Flint Xiaofeng Fan; Yining Ma 001; Zhongxiang Dai; Cheston Tan; Bryan Kian Hsiang Low (AAMAS, 2023)

Federated Neural Bandits. - Zhongxiang Dai; Yao Shu; Arun Verma; Flint Xiaofeng Fan; Bryan Kian Hsiang Low; Patrick Jaillet (ICLR, 2023)

FedHQL: Federated Heterogeneous Q-Learning. - Flint Xiaofeng Fan; Yining Ma 001; Zhongxiang Dai; Cheston Tan; Bryan Kian Hsiang Low; Roger Wattenhofer (CoRR, 2023)

Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning. - Xinyang Lu; Flint Xiaofeng Fan; Tianying Wang (CoRR, 2023)

Decentralized Federated Policy Gradient with Byzantine Fault-Tolerance and Provably Fast Convergence. - Philip Jordan; Florian Grötschla; Flint Xiaofeng Fan; Roger Wattenhofer (AAMAS, 2024)

SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems. - Nathan Corecco; Giorgio Piatti; Luca A. Lanzendörfer; Flint Xiaofeng Fan; Roger Wattenhofer (ECAI, 2024)

CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening. - Hei Yi Mak; Flint Xiaofeng Fan; Luca A. Lanzendörfer; Cheston Tan; Wei Tsang Ooi; Roger Wattenhofer (CoRR, 2024)

An LLM-based Recommender System Environment. - Nathan Corecco; Giorgio Piatti; Luca A. Lanzendörfer; Flint Xiaofeng Fan; Roger Wattenhofer (CoRR, 2024)

Quality Diversity Imitation Learning. - Zhenglin Wan; Xingrui Yu; David Mark Bossens; Yueming Lyu; Qing Guo 005; Flint Xiaofeng Fan; Ivor W. Tsang (CoRR, 2024)

FedRLHF: A Convergence-Guaranteed Federated Framework for Privacy-Preserving and Personalized RLHF. - Flint Xiaofeng Fan; Cheston Tan; Yew-Soon Ong; Roger Wattenhofer; Wei Tsang Ooi (AAMAS, 2025)

FedHPD: Heterogeneous Federated Reinforcement Learning via Policy Distillation. - Wenzheng Jiang; Ji Wang 002; Xiongtao Zhang; Weidong Bao 001; Cheston Tan; Flint Xiaofeng Fan (AAMAS, 2025)

Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs. - Flint Xiaofeng Fan; Cheston Tan; Roger Wattenhofer; Yew-Soon Ong (CoRR, 2025)