s-step self-distillation is optimal among spectral shrinkage estimators for s-spiked covariance matrices and necessary for optimality.
Federated distillation: A survey
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
EdgeFD uses a KMeans-based client-side filter to improve federated distillation accuracy close to IID levels on non-IID data distributions for resource-constrained edge devices.
A heuristic using server learning plus filtering and geometric median aggregation maintains high accuracy in federated learning with over 50% malicious clients and small non-matching server data.
FedKD-hybrid is a hybrid federated knowledge distillation framework for multi-model lithography hotspot detection that outperforms prior methods on ICCAD-2012 and real-world FAB datasets.
citing papers explorer
-
Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models
s-step self-distillation is optimal among spectral shrinkage estimators for s-spiked covariance matrices and necessary for optimality.
-
Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
EdgeFD uses a KMeans-based client-side filter to improve federated distillation accuracy close to IID levels on non-IID data distributions for resource-constrained edge devices.
-
Enhancing Robustness of Federated Learning via Server Learning
A heuristic using server learning plus filtering and geometric median aggregation maintains high accuracy in federated learning with over 50% malicious clients and small non-matching server data.
-
Federated Knowledge Distillation for Multi-Model Architectures Lithography Hotspot Detection
FedKD-hybrid is a hybrid federated knowledge distillation framework for multi-model lithography hotspot detection that outperforms prior methods on ICCAD-2012 and real-world FAB datasets.