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Found 5 papers, 4 papers with code
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier
Recent advances in neural collapse have shown that the classifiers and feature prototypes under perfect training scenarios collapse into an optimal structure called simplex equiangular tight frame (ETF).
Federated Semi-Supervised Learning with Annotation Heterogeneity
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data.
Revisiting Weighted Aggregation in Federated Learning with Neural Networks
In federated learning (FL), weighted aggregation of local models is conducted to generate a global model, and the aggregation weights are normalized (the sum of weights is 1) and proportional to the local data sizes.
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation
Federated learning provides a privacy guarantee for generating good deep learning models on distributed clients with different kinds of data.
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features
Experiments on several benchmark datasets show that the proposed CReFF is an effective solution to obtain a promising FL model under heterogeneous and long-tailed data.
