FedRef uses a temporally aggregated reference model and MAP regularization for server-side fine-tuning to reduce forgetting and drift in non-IID federated learning, showing better accuracy and lower client compute on image tasks.
Federated optimization in heterogeneous networks,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
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
-
FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning
FedRef uses a temporally aggregated reference model and MAP regularization for server-side fine-tuning to reduce forgetting and drift in non-IID federated learning, showing better accuracy and lower client compute on image tasks.
-
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.