Local Adaptive Federated Learning with Channel Personalized Normalization
To relieve the impact of data heterogeneity problems caused by full overlapping attribute skew between clients in Federated Learning(FL),a local adaptive FL algorithm that incorporates channel personalized normalization is proposed in this paper.Specifically,an FL model oriented to data attribute skew is constructed,and a series of random enhancement operations are performed on the images data set in the client before training begins.Next,the client calculates the mean and standard deviation of the data set separately by color channel to achieve channel personalized normalization.Furthermore,a local adaptive update FL algorithm is designed,that is,the global model and the local model are adaptively aggregated for local initialization.The uniqueness of this aggregation method is that it not only retains the personalized characteristics of the client model,but also can capture necessary information in the global model to improve the generalization performance of the model.Finally,the experimental results demonstrate that the proposed algorithm obtains competitive convergence speed compared with existing representative works and the accuracy is 3%～19%higher.
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