Derives tractable optimal fair multi-class classifier and supplies in-processing and post-processing algorithms that converge to the accuracy-fairness Pareto frontier.
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2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
FedDTL decouples VLM encoders across server and clients with modality alignment and uses two-stage local fine-tuning (supervised then RL) to balance global adaptation and generalization in heterogeneous federated learning.
citing papers explorer
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Demystifying the Optimal Fair Classifier in Multi-Class Classification
Derives tractable optimal fair multi-class classifier and supplies in-processing and post-processing algorithms that converge to the accuracy-fairness Pareto frontier.
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Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning
FedDTL decouples VLM encoders across server and clients with modality alignment and uses two-stage local fine-tuning (supervised then RL) to balance global adaptation and generalization in heterogeneous federated learning.