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.
We further introduce label skew (IID, Dirichlet(0.1), Dirichlet(0.3), Dirichlet(0.5)) on top of the feature shift
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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.