Federated Transformer Hawkes Processes for Distributed Event Sequence Prediction
Paper
Wang, Xinyu, Qiang, Feng, Ma, Li, Zhang, Peng, Yang, Hong Yang, Li, Zhao and Zhang, Ji. 2024. "Federated Transformer Hawkes Processes for Distributed Event Sequence Prediction." 2024 International Joint Conference on Neural Networks (IJCNN). Yokohama, Japan 30 Jun - 05 Jul 2024 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN60899.2024.10651498
Paper/Presentation Title | Federated Transformer Hawkes Processes for Distributed Event Sequence Prediction |
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Presentation Type | Paper |
Authors | Wang, Xinyu, Qiang, Feng, Ma, Li, Zhang, Peng, Yang, Hong Yang, Li, Zhao and Zhang, Ji |
Journal or Proceedings Title | Proceedings of 2024 International Joint Conference on Neural Networks (IJCNN) |
Number of Pages | 8 |
Year | 2024 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISBN | 9798350359312 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/IJCNN60899.2024.10651498 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/10651498 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/10649807/proceeding |
Conference/Event | 2024 International Joint Conference on Neural Networks (IJCNN) |
Event Details | 2024 International Joint Conference on Neural Networks (IJCNN) Parent International Joint Conference on Neural Networks (IJCNN) Delivery In person Event Date 30 Jun 2024 to end of 05 Jul 2024 Event Location Yokohama, Japan |
Abstract | Uncovering temporal dependency patterns behind event sequences plays a key role in predicting event types and event times. Recently, transformers based models have been used to describe point processes, such as the Transformer Hawkes Processes (THP models). However, existing THP models assume that data are collected in a central server and can be always seen during model training. Indeed, event sequence data are often located at different data centers which can not be shared directly due to the risk of privacy leakage. To this end, we combine in this paper the THP models with federated learning, enabling collaborative learning from a large amount of distributed event sequence data. Experiments show that our approach surpasses single data source training while preserving data privacy. For clients lacking certain types of event sequence data, our method performs much more stable than previous centralized training models. © 2024 IEEE. |
Keywords | Event Prediction |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Guangzhou University, China |
Shenzhen Weiyan Technology, China | |
Zhejiang Lab, China | |
University of Southern Queensland |
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https://research.usq.edu.au/item/zqzq1/federated-transformer-hawkes-processes-for-distributed-event-sequence-prediction
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