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• A*STAR Computing and Information Science Scholarship (ACIS)
• A*STAR International Fellowship (AIF)
• NUS Computing Research Achievement Award
• AAMAS 2024 Student Scholarship

1. Zhenglin Wan, Xingrui Yu, David Mark Bossens, Yueming Lyu, Qing Guo, Flint Xiaofeng Fan, Yew-Soon Ong, Ivor W Tsang. Diversifying Robot Locomotion Behaviors with Extrinsic Behavioural Curiosity. ICML, 2025.

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

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

4. Wenzheng Jiang, Ji Wang, Xiongtao Zhang, Weidong Bao, Cheston Tan, and Flint Xiaofeng Fan. FedHPD: Heterogeneous Federated Reinforcement Learning via Policy Distillation. AAMAS, 2025.

5. Nathan Corecco, Giorgio Piatti, Luca A Lanzend ̈orfer, Flint Xiaofeng Fan, and Roger Wattenhofer. SUBER: An RL Environment with Simulated Human Behaviour for Recommender Systems. ECAI, 2024.

6. Zhongxiang Dai, Flint Xiaofeng Fan, Cheston Tan, Trong Nghia Hoang, Bryan Kian Hsiang Low, and Patrick Jaillet. Federated Sequential Decision-Making: Bayesian Optimisation, Reinforcement Learning, and Beyond. Federated Learning (pp. 257-279). Academic Press, Elsevier.

7. Philip Jordan, Florian Gr ̈otschla, Flint Xiaofeng Fan, and Roger Wattenhofer. Decentralised Federated Policy Gradient with Byzantine Fault-Tolerance and Provably Fast Convergence. AAMAS, 2024.

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

9. Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, and Patrick Jaillet. Federated Neural Bandit. ICLR, 2023.

10. Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, and Bryan Kian Hsiang Low. Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantees. NeurIPS, 2021.

• Conference PC Member: IJCNN: 2025 (Area Chair); AAMAS: 2023, 2025 (Session Chair); ALA: 2024 (Session Chair), 2025; IJCAI: 2024; NeurIPS: 2023, 2025; AISTATS: 2023, 2024, 2025; ICLR: 2024, 2025; IEEE ICRA: 2022,2023; IEEE MRS: 2021; IEEE IROS: 2021,2022,2023; IEEE CAI: 2024
• Journal reviewer: INFORMS Operations Research (OR); IEEE Transactions on Big Data (TBD); IEEE Transactions on Neural Networks and Learning Systems (TNNLS); IEEE Robotics and Automation Letters (RA-L)