Books & Book Chapters

- 2025 -

1. Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). (2025). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, p. 182. Springer, Cham.

2. Li, Z., Wu, X., Tang, X., He, T., Ong, Y.-S., Chen, M., Liu, Q., Lao, Q. & Yu, H. (2025). Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning. In: Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, pp. 77–92. Springer, Cham.

- 2024 -

1. Yu, H. & Tang, X. (2024). Mitigating Collusive Behaviours in Open Data Exchange Systems. In: Chellam, R. (Ed.).  Artificial Intelligence Ethics & Governance Body of Knowledge, vol. 2, SCS. 

2. Lu, S., Xing, P. & Yu, H. (2024). Graph-Aware Federated Learning. In: Federated Learning: Theory and Practice, pp. 181–197. Elsevier.

- 2023 -

3. Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). (2023). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, p. 158. Springer, Cham.

- 2020 -

1. Yang, Q., Fan, L. & Yu, H. (Eds.). (2020). Federated Learning: Privacy and Incentive. Lecture Notes in Computer Science, vol. 12500, p. 282. Springer, Cham.

1. Lyu, L., Yu, H., Zhao, J. & Yang, Q. (2020). Threats to Federated Learning. In: Yang, Q., Fan, L. & Yu, H. (Eds.). Federated Learning: Privacy and Incentive, pp. 1–14. Springer, Cham.

2. Chen, Y., Wang, X., Qin, X., Yu, H., Chen, B. & Shen, Z. (2020). Dealing with Label Quality Disparity in Federated Learning. In: Yang, Q., Fan, L. & Yu, H. (Eds.). Federated Learning: Privacy and Incentive, pp. 106–120. Springer, Cham.

3. Liu, Y., Ai, Z., Sun, S., Zhang, S., Liu, Z. & Yu, H. (2020). FedCoin: A Peer-to-Peer Payment System for Federated Learning. In: Yang, Q., Fan, L. & Yu, H. (Eds.). Federated Learning: Privacy and Incentive, pp. 121–134. Springer, Cham.

4. Chen, Z., Liu, Z., Ng, K. L., Yu, H., Liu, Y. & Yang, Q. (2020). A Gamified Research Tool for Incentive Mechanism Design in Federated Learning. In: Yang, Q., Fan, L. & Yu, H. (Eds.). Federated Learning: Privacy and Incentive, pp. 164–171. Springer, Cham.

5. Lyu, L., Xu, X., Wang, Q. & Yu, H. (2020). Collaborative Fairness in Federated Learning. In: Yang, Q., Fan, L. & Yu, H. (Eds.). Federated Learning: Privacy and Incentive, pp. 185–199. Springer, Cham.

6. Cong, M., Yu, H., Weng, X. & Yiu, S.-M. (2020). A Game-Theoretic Framework for Incentive Mechanism Design in Federated Learning. In: Yang, Q., Fan, L. & Yu, H. (Eds.). Federated Learning: Privacy and Incentive, pp. 200–217. Springer, Cham.

7. Yu, H., Weng, J., Ong, Y. S. & Cong, G. (2020). Data Management. In: Chellam, R. (Ed.). Artificial Intelligence Ethics & Governance Body of Knowledge, vol. 1, SCS.

8. 杨强、刘洋、程勇、 康炎、陈天健、于涵  《联邦学习》 电子工业出版社，p. 208，2020.

1. Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T. & Yu, H. (2020). Federated Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, p. 189. Springer, Cham. (Access via NTU Library)

- 2018 -

1. Yu, H., Miao, C., An, B., Shen, Z. & Leung, C. (2018). Making Efficient Reputation-aware Decisions in Multi-agent Systems. In: Hao, J. & Leung, H.-F. (Eds.). Interactions in Multiagent Systems, pp. 43–64. World Scientific, Singapore.

- 2010 -

1. Tao, X., Shen, Z., Miao, C., Theng, Y. L., Miao, Y. & Yu, H. (2010). Automated Negotiation through a Cooperative-Competitive Model. In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. & Yamaki, H. (Eds.). Innovations in Agent-Based Complex Automated Negotiations. Studies in Computational Intelligence, vol. 319, pp. 161–178. Springer, Berlin, Heidelberg.