UA-PDFL measures client data skew using model outputs on a fixed unit input and switches between layer-wise personalization and whole-model replacement, improving decentralized federated learning accuracy on non-IID data.
Federated learning with hierar- chical clustering of local updates to improve training on non-iid data, in: 2020 International Joint Conference on Neural Networks (IJCNN), IEEE
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UA-PDFL: A Personalized Approach for Decentralized Federated Learning
UA-PDFL measures client data skew using model outputs on a fixed unit input and switches between layer-wise personalization and whole-model replacement, improving decentralized federated learning accuracy on non-IID data.