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 • 25 Jul 2023 • Leiming Chen, Weishan Zhang, Cihao Dong, Sibo Qiao, Ziling Huang, Yuming Nie, Zhaoxiang Hou, Chee Wei Tan
Traditional federated learning uses the number of samples to calculate the weights of each client model and uses this fixed weight value to fusion the global model.
no code implementations • 29 Dec 2021 • Bingyang Chen, Tao Chen, Xingjie Zeng, Weishan Zhang, Qinghua Lu, Zhaoxiang Hou, Jiehan Zhou, Sumi Helal
Additionally, a dynamic-weight based fusion strategy is proposed to further improve the accuracy of federated learning, which dynamically selects clients based on the accuracy of each local model.