Unbiased federated learning model for skin lesion classification
Federated Learning
Multimodal images
Domain Adaptation
Debiased Method
TABE: use custom loss which minimize the main classification loss, and train additional classifier using confusion loss between additional class label and uniform distribution with the same feature representation
Additional fusion branch
Multi-scale Fully-shared Fusion Network Multi-scale fusion structure combines deep and shallow features within individual modalities, and Dermo-Clinical Block (DCB) integrates the feature maps from dermoscopic images and clinical images
Domain-Adversarial Training of Neural Networks (Gradient Reversal) Train a domain classifier and multiplies the gradient by a certain negative constant during the backpropagation-based training on feature extractor layer's weights.
Late fusion
Performance Measurement
Accuracy benefit of FL: accuracy of (FL model - local model) on local test set
Fairness benefit of FL: fairness of (FL model - local model) on local test set. Fairness is measured using equalized odds (Hardt et al., 2016) and demographic parity (Dwork et al., 2012)
Communication efficiency
Local updating
Compression schemes
Decentralized training
Systems heterogeneity
Topology
Statistical heterogeneity
Asynchronous communication
Active sampling
Fault tolerance
Modeling heterogeneous data
Bias reduction
Reveal bias
Fairness
Refine client update
Regularize the local objective function
Personalized FL
click to edit