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Found 3 papers, 1 papers with code
Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
Federated learning (FL) inevitably confronts the challenge of system heterogeneity in practical scenarios.
FedIN: Federated Intermediate Layers Learning for Model Heterogeneity
In this study, we propose an FL method called Federated Intermediate Layers Learning (FedIN), supporting heterogeneous models without relying on any public dataset.
Exploiting Features and Logits in Heterogeneous Federated Learning
Felo averages the mid-level features and logits from the clients at the server based on their class labels to provide the average features and logits, which are utilized for further training the client models.
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