Federated weighted domain adaptation for multi-site autism diagnosis
To address the lack of attention to data heterogeneity and solve the local blocking problem caused by the lack of labeled data at some sites in Federated Learning(FL)multi-site autism diagnosis methods.Federated Weighted Domain Adaptation(FedWDA)has been proposed.Firstly,instead of aggregating a global model,FedWDA retains the Batch Normalization(BN)layer for each local model and calculates the similarity between the BN layer statistics with Earth Mover's Distance(EMD)to guide the models'weighted aggregation.Each site is provided with a more personal-ized local model,alleviating the problem of poor accuracy caused by data heterogeneity.Secondly,unsupervised cluster learning is used to fine-tune the local models of sites lacking labeled data,based on the pseudo-label's consistency.The experimental results on ABIDE show that the proposed method can perform multi-site autism diagnosis more accurately than other traditional methods.
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