Data sharing for semi-asynchronous federated learning
In the semi-asynchronous scenario,there is a problem of unbalanced frequency when each unit uses the federated learning data-sharing method for training because the response time of each unit is quite different.Units with less response time can update the model with faster training frequen-cy,so in this case it is difficult to effectively learn knowledge from units with less response time,which affects its performance.To solve this problem,a federated learning algorithm that senses the distribution of response time was proposed.By grouping the units participating in the training,a di-rected acyclic graph was used to guide each training group to perform iterative training within the group in a parallel or serial manner,so as to achieve a balanced training frequency.Experimental re-sults show that the above mentioned method significantly improves the training efficiency and model prediction compared with the traditional semi-asynchronous federated learning data-sharing method.
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