Abstract: The present disclosure relates to a data processing method, non-transitory medium and electronic device. The method includes: acquiring first user data and second user data, and initializing a first time window corresponding to the first user data and first time information corresponding to the first time window, as well as a second time window corresponding to the second user data and second time information corresponding to the second time window; determining first data and the first time information corresponding to the first time window based on the first user data; determining second data and the second time information corresponding to the second time window based on the second user data; based on the first data and the second data, determining alignment data corresponding to a same user from the first user data and the second user data.

Abstract: A method and system for distributed machine learning and model training are disclosed. In particular, a finite asynchronous parallel training scheme is described. The finite asynchronous parallel training takes advantage of the benefits of both asynchronous parallel training and synchronous parallel training. The computation delays in various distributed computation nodes are further considered when training parameter are updated during each round of iterative training. The disclosed method and system facilities increase of model training speed and efficiency.

Abstract: A method and system for distributed machine learning and model training are disclosed. In particular, a finite asynchronous parallel training scheme is described. The finite asynchronous parallel training takes advantage of the benefits of both asynchronous parallel training and synchronous parallel training. The computation delays in various distributed computation nodes are further considered when training parameter are updated during each round of iterative training. The disclosed method and system facilities increase of model training speed and efficiency.