LIPS, a method that periodically prunes low-sensitivity middle-layer weights after aggregation, mitigates layer-wise inertia and improves low-data federated learning accuracy.
Sparsity winning twice: Better robust generalization from more efficient training
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Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity
LIPS, a method that periodically prunes low-sensitivity middle-layer weights after aggregation, mitigates layer-wise inertia and improves low-data federated learning accuracy.