PLayer-FL picks the layer split in partial federated learning from a low-cost sensitivity metric computed at epoch 1, and reports competitive F1, fairness, and participation incentives across seven non-IID datasets.
Federated learning with hierarchical clustering of local updates to improve training on non-iid data
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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning
PLayer-FL picks the layer split in partial federated learning from a low-cost sensitivity metric computed at epoch 1, and reports competitive F1, fairness, and participation incentives across seven non-IID datasets.