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PerHeFed: A general framework of personalized federated learning for heterogeneous convolutional neural networks
8
Zitationen
4
Autoren
2022
Jahr
Abstract
of the shared sub-model parameters with only a 4.38% drop in accuracy on SVHN dataset and on CIFAR-10, PerHeFed even achieves a 0.3% improvement in accuracy. To the best of our knowledge, our work is the first general personalized federated learning framework for heterogeneous convolutional networks, even cross different networks, addressing model structure unity in conventional federated learning.
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