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11.发明申请公开(公告)号：US20220383198A1
公开(公告)日：2022-12-01
申请号：US17879888
申请日：2022-08-03
Inventor： Ji LIU , Chendi ZHOU , Shilei JI , Dejing DOU
Abstract: The present disclosure provides a method for asynchronous federated learning, including: in response to a request for participating in asynchronous federated learning sent by a target electronic device, determining, according to performance information of a server, a first number of electronic devices that the server supports to participate in the asynchronous federated learning, and acquiring a second number of other electronic devices that have participated in the asynchronous federated learning; if the first number is greater than the second number, sending a global model to be optimized to the target electronic device, and receiving target feedback information which is obtained by the target electronic device from training on the global model to be optimized; and optimizing, according to the target feedback information, the global model to be optimized to obtain an optimized global model.
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公开(公告)号：US20230084055A1
公开(公告)日：2023-03-16
申请号：US17991958
申请日：2022-11-22
Inventor： Ji LIU , Sunjie YU , Dejing DOU , Jiwen ZHOU
Abstract: A method for generating a federated learning model is provided. The method includes obtaining images; obtaining sorting results of the images; and generating a trained federated learning model by training a federated learning model to be trained according to the images and the sorting results. The federated learning model to be trained is obtained after pruning a federated learning model to be pruned, and a pruning rate of a convolution layer in the federated learning model to be pruned is automatically adjusted according to a model accuracy during the pruning.
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Patent Agency Ranking
公开(公告)号：US20230083116A1
公开(公告)日：2023-03-16
申请号：US17988264
申请日：2022-11-16
Inventor： Ji LIU , Hong ZHANG , Juncheng JIA , Jiwen ZHOU , Shengbo PENG , Ruipu ZHOU , Dejing DOU
Abstract: A federated learning method and system, an electronic device, and a storage medium, which relate to a field of artificial intelligence, in particular to fields of computer vision and deep learning technologies. The method includes: performing a plurality of rounds of training until a training end condition is met, to obtain a trained global model; and publishing the trained global model to a plurality of devices. Each of the plurality of rounds of training includes: transmitting a current global model to at least some devices in the plurality of devices; receiving trained parameters for the current global model from the at least some devices; performing an aggregation on the received parameters to obtain a current aggregation model; and adjusting the current aggregation model based on a globally shared dataset, and updating the adjusted aggregation model as a new current global model for a next round of training.
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公开(公告)号：US20220391780A1
公开(公告)日：2022-12-08
申请号：US17820758
申请日：2022-08-18
Inventor： Ji LIU , Beichen MA , Chendi ZHOU , Juncheng JIA , Dejing DOU , Shilei JI , Yuan LIAO
Abstract: The present disclosure provides a method of federated learning. A specific implementation solution includes: determining, for a current learning period, a target device for each task of at least one learning task to be performed, from a plurality of candidate devices according to a plurality of resource information of the plurality of candidate devices; transmitting a global model for the each task to the target device for the each task, so that the target device for the each task trains the global model for the each task; and updating, in response to receiving trained models from all target devices for the each task, the global model for the each task according to the trained models, so as to complete the current learning period. The present disclosure further provides an electronic device, and a storage medium.
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15.发明申请公开(公告)号：US20220374776A1
公开(公告)日：2022-11-24
申请号：US17868113
申请日：2022-07-19
Inventor： Ji LIU , Beichen MA , Chendi ZHOU , Jingbo ZHOU , Ruipu ZHOU , Dejing DOU
IPC: G06N20/00
Abstract: The present disclosure provides a method and apparatus for federated learning, which relate to the technical fields such as big data and deep learning. A specific implementation is: generating, for each task in a plurality of different tasks trained simultaneously, a global model for each task; receiving resource information of each available terminal in a current available terminal set; selecting a target terminal corresponding to each task from the current available terminal set, based on the resource information and the global model; and training the global model using the target terminal until a trained global model for each task meets a preset condition.
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公开(公告)号：US20220374775A1
公开(公告)日：2022-11-24
申请号：US17867516
申请日：2022-07-18
Inventor： Ji LIU , Beichen MA , Jingbo ZHOU , Ruipu ZHOU , Dejing DOU
Abstract: A method for multi-task scheduling, a device and a storage medium are provided. The method may include: initializing a list of candidate scheduling schemes, the candidate scheduling scheme being used to allocate a terminal device for training to each machine learning task in a plurality of machine learning tasks; perturbing, for each candidate scheduling scheme in the list of candidate scheduling schemes, the candidate scheduling scheme to generate a new scheduling scheme; determining whether to replace the candidate scheduling scheme with the new scheduling scheme based on a fitness value of the candidate scheduling scheme and a fitness value of the new scheduling scheme, to generate a new scheduling scheme list; and determining a target scheduling scheme, based on the fitness value of each new scheduling scheme in the new scheduling scheme list.
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公开(公告)号：US20220366320A1
公开(公告)日：2022-11-17
申请号：US17864098
申请日：2022-07-13
Inventor： Ji LIU , Chendi ZHOU , Juncheng JIA , Dejing DOU
IPC: G06N20/20
Abstract: A computer-implemented method is provided. The method includes: executing, for each task in a federated learning system, a first training process comprising: obtaining resource information of a plurality of terminal devices of the federated learning system; determining one or more target terminal devices corresponding to the task based on the resource information; and training a global model corresponding to the task by the target terminal devices until the global model meets a preset condition.
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