no code implementations • 23 Apr 2024 • Chao Ren, Han Yu, Hongyi Peng, Xiaoli Tang, Anran Li, Yulan Gao, Alysa Ziying Tan, Bo Zhao, Xiaoxiao Li, Zengxiang Li, Qiang Yang
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while addressing concerns of privacy, data decentralization, and computational efficiency.
no code implementations • 8 Apr 2024 • Zhengyang Zhao, Haitao Yuan, Nan Jiang, Minxiao Chen, Ning Liu, Zengxiang Li
Accurate Traffic Prediction is a challenging task in intelligent transportation due to the spatial-temporal aspects of road networks.
no code implementations • 10 Jan 2024 • Shubao Zhao, Ming Jin, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Qingsong Wen, Yi Wang
Time series forecasting is crucial and challenging in the real world.
no code implementations • 22 Aug 2023 • Zengxiang Li, Zhaoxiang Hou, Hui Liu, Ying Wang, Tongzhi Li, Longfei Xie, Chao Shi, Chengyi Yang, Weishan Zhang, Zelei Liu, Liang Xu
Preliminary experiments show that enterprises can enhance and accumulate intelligent capabilities through multimodal model federated learning, thereby jointly creating an smart city model that provides high-quality intelligent services covering energy infrastructure safety, residential community security, and urban operation management.
no code implementations • 7 Aug 2023 • Yulan Gao, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Han Yu
Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners.
no code implementations • 1 Jul 2023 • Has Sun, Xiaoli Tang, Chengyi Yang, Zhenpeng Yu, Xiuli Wang, Qijie Ding, Zengxiang Li, Han Yu
Federated learning (FL) offers a solution to this problem by enabling local data processing on each participant, such as gas companies and heating stations.
no code implementations • 22 Feb 2023 • YuanYuan Chen, Zichen Chen, Sheng Guo, Yansong Zhao, Zelei Liu, Pengcheng Wu, Chengyi Yang, Zengxiang Li, Han Yu
Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications.
no code implementations • 17 Nov 2022 • Sheng Guo, Zengxiang Li, Hui Liu, Shubao Zhao, Cheng Hao Jin
Intelligent fault diagnosis is essential to safe operation of machinery.
no code implementations • 8 Mar 2022 • Lianlian Jiang, Yuexuan Wang, Wenyi Zheng, Chao Jin, Zengxiang Li, Sin G. Teo
In this work, we propose a new approach, LSTMSPLIT, that uses SL architecture with an LSTM network to classify time-series data with multiple clients.
no code implementations • 25 May 2020 • Renuga Kanagavelu, Zengxiang Li, Juniarto Samsudin, Yechao Yang, Feng Yang, Rick Siow Mong Goh, Mervyn Cheah, Praewpiraya Wiwatphonthana, Khajonpong Akkarajitsakul, Shangguang Wangz
Countries across the globe have been pushing strict regulations on the protection of personal or private data collected.
Distributed, Parallel, and Cluster Computing
no code implementations • 17 Mar 2020 • Huafei Zhu, Zengxiang Li, Mervyn Cheah, Rick Siow Mong Goh
In the second fold, an oracle-aided MPC solution for computing weighted federated learning is formalized by decoupling the security of federated learning systems from that of underlying multi-party computations.
Cryptography and Security
no code implementations • 26 Jun 2019 • Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato, Zengxiang Li, Lingjuan Lyu, Yingbo Liu
To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data.