FeDa4Fair is a new library and benchmark for creating federated datasets with heterogeneous client-level biases to standardize evaluation of fairness methods in federated learning.
Salvaging federated learning by local adaptation
3 Pith papers cite this work. Polarity classification is still indexing.
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FMCL performs one-shot class-aware client clustering in heterogeneous federated learning by deriving semantic signatures from foundation model embeddings and using cosine distance, yielding improved performance and stable clusters compared to prior methods.
FedRio is a new federated framework that outperforms standard federated baselines in social bot detection accuracy and efficiency while staying competitive with centralized models under stronger privacy constraints.
citing papers explorer
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FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation
FeDa4Fair is a new library and benchmark for creating federated datasets with heterogeneous client-level biases to standardize evaluation of fairness methods in federated learning.
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FMCL: Class-Aware Client Clustering with Foundation Model Representations for Heterogeneous Federated Learning
FMCL performs one-shot class-aware client clustering in heterogeneous federated learning by deriving semantic signatures from foundation model embeddings and using cosine distance, yielding improved performance and stable clusters compared to prior methods.
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FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillation
FedRio is a new federated framework that outperforms standard federated baselines in social bot detection accuracy and efficiency while staying competitive with centralized models under stronger privacy constraints.