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.
Salvaging federated learning by local adaptation
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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.
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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.