| FedProto: Federated Prototype Learning Across Heterogeneous Clients Y Tan, G Long, L Liu, T Zhou, Q Lu, J Jiang, C Zhang AAAI Conference on Artificial Intelligence, AAAI-22 36 (8), 8432-8440, 2022 | 941 | 2022 |
| Federated learning for open banking G Long, Y Tan, J Jiang, C Zhang Federated learning: privacy and incentive, 240-254, 2020 | 508 | 2020 |
| Deep reinforcement learning for autonomous internet of things: Model, applications and challenges L Lei, Y Tan, K Zheng, S Liu, K Zhang, X Shen IEEE Communications Surveys & Tutorials 22 (3), 1722-1760, 2020 | 343 | 2020 |
| Federated Learning from Pre-Trained Models: A Contrastive Learning Approach Y Tan, G Long, J Ma, L Liu, T Zhou, J Jiang Advances in Neural Information Processing Systems, NeurIPS-22, 2022 | 308 | 2022 |
| Federated Learning on Non-IID Graphs via Structural Knowledge Sharing Y Tan, Y Liu, G Long, J Jiang, Q Lu, C Zhang AAAI Conference on Artificial Intelligence, AAAI-23, 2022 | 239 | 2022 |
| Emerging trends in federated learning: From model fusion to federated x learning S Ji, Y Tan, T Saravirta, Z Yang, Y Liu, L Vasankari, S Pan, G Long, ... International Journal of Machine Learning and Cybernetics, 2024 | 162 | 2024 |
| Dynamic energy dispatch based on deep reinforcement learning in IoT-driven smart isolated microgrids L Lei, Y Tan, G Dahlenburg, W Xiang, K Zheng IEEE internet of things journal 8 (10), 7938-7953, 2020 | 140 | 2020 |
| Federated learning for privacy-preserving open innovation future on digital health G Long, T Shen, Y Tan, L Gerrard, A Clarke, J Jiang Humanity driven AI: productivity, well-being, sustainability and partnership …, 2021 | 109 | 2021 |
| Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning Y Tan, C Chen, W Zhuang, X Dong, L Lyu, G Long Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 40 | 2023 |
| LSTM-based anomaly detection for non-linear dynamical system Y Tan, C Hu, K Zhang, K Zheng, EA Davis, JS Park arXiv preprint arXiv:2006.03193, 2020 | 35 | 2020 |
| Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems X Shen, Y Liu, Y Dai, Y Wang, R Miao, Y Tan, S Pan, X Wang arXiv preprint arXiv:2505.23352, 2025 | 17 | 2025 |
| Bisecle: Binding and Separation in Continual Learning for Video Language Understanding Y Tan, X Hu, H Xue, C De Melo, FD Salim arXiv preprint arXiv:2507.00469, 2025 | 7 | 2025 |
| Blindguard: Safeguarding llm-based multi-agent systems under unknown attacks R Miao, Y Liu, Y Wang, X Shen, Y Tan, Y Dai, S Pan, X Wang arXiv preprint arXiv:2508.08127, 2025 | 5 | 2025 |
| A survey of generalization of graph anomaly detection: From transfer learning to foundation models J Pan, Y Zheng, Y Tan, Y Liu arXiv preprint arXiv:2509.06609, 2025 | 4 | 2025 |
| Influence-oriented personalized federated learning Y Tan, G Long, J Jiang, C Zhang arXiv preprint arXiv:2410.03315, 2024 | 4 | 2024 |
| Taming heterogeneity to deal with test-time shift in federated learning Y Tan, C Chen, W Zhuang, X Dong, L Lyu, G Long International Workshop on Federated Learning for Distributed Data Mining, 2023 | 4 | 2023 |
| An in-vehicle keyword spotting system with multi-source fusion for vehicle applications Y Tan, K Zheng, L Lei 2019 IEEE Wireless Communications and Networking Conference (WCNC), 1-6, 2019 | 3 | 2019 |
| Multi-Stage Verification-Centric Framework for Mitigating Hallucination in Multi-Modal RAG B Chen, W Wongso, X Hu, Y Tan, F Salim arXiv preprint arXiv:2507.20136, 2025 | 2 | 2025 |
| Federated Representation Learning across Heterogeneous Clients Y Tan PQDT-Global, 2024 | | 2024 |
| Federated Learning from Pre-Trained Models: A Contrastive Learning Approach Y Tan, G Long, J Ma, L Liu, T Zhou, J Jiang ICML Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward, 2022, 0 | | |
Guodong LongAssociate Professor, Faculty of Engineering and IT, University of Technology SydneyVerified email at uts.edu.au
