Key result
A novel personalized federated learning method with feature alignment demonstrated better adaptation to local data and superior generalization ability for ECG classification.
Why the study?
Does a personalized federated learning method with feature alignment improve ECG classification performance on local data?
Does a personalized federated learning method with feature alignment improve ECG classification performance on local data?
A novel personalized federated learning approach with feature alignment improves ECG classification performance while addressing data privacy and local deployment challenges.
May aid privacy-preserving local ECG classification; leaves open prospective clinical validation.
Electrocardiogram (ECG) data classification is a hot research area for its application in medical information processing. However, insufficient data, privacy preserve, and local deployment are still challenging difficulties. To address these problems, a novel personalized federated learning method for ECG classification is proposed in this paper. First, a global model is trained with federated learning framework on multiple local data clients. Then, we use the global model and private data to train the local model. To reduce the feature inconsistency between global and private local data and for better fitting the private local data, a novel ”feature alignment” module is devised to guarantee the uniformity, which contains two parts, global alignment and local alignment, respectively. For global alignment, the graph metric of batch data is used to constrain the dissimilarity between features generated by the global model and local model. For local alignment, triplet loss is adopted to increase discriminative ability for local private data. Comprehensive experiments on our collected dataset are evaluated. The results show that the proposed method can be better adapted to local data and exhibit superior ability of generalization.
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Tang et al. (2021) studied Electrocardiogram (ECG) classification. Personalized federated learning method with feature alignment was evaluated on ECG classification performance and generalization ability. A novel personalized federated learning method with feature alignment demonstrated better adaptation to local data and superior generalization ability for ECG classification.
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