Abstract: A first node obtains local processing time separately corresponding to a plurality of second nodes and/or uploading time of local processing results separately corresponding to the plurality of second nodes. The first node soils sequence numbers of the plurality of second nodes based on the local processing time and/or the uploading time, to obtain a first sequence. The first node determines a second sequence based on the first sequence, where the second sequence includes some or all second node sequence numbers in the first sequence. The first node schedules second nodes corresponding to the sequence numbers included in the second sequence.

Abstract: A model training method includes performing, by an ith device in a kth group of devices, n*M times of model training. The ith device completes a model parameter exchange with at least one other device in the kth group of devices every M times of model training, M is a quantity of devices in the kth group of devices, M is greater than or equal to 2, and n is an integer. The model training method also includes sending, by the ith device, a model Mi,n*M to a target device. The model Mi,n*M is obtained by the ith device by completing the n*M times of model training.

Abstract: A first node obtains local processing time separately corresponding to a plurality of second nodes and/or uploading time of local processing results separately corresponding to the plurality of second nodes. The first node soils sequence numbers of the plurality of second nodes based on the local processing time and/or the uploading time, to obtain a first sequence. The first node determines a second sequence based on the first sequence, where the second sequence includes some or all second node sequence numbers in the first sequence. The first node schedules second nodes corresponding to the sequence numbers included in the second sequence.