FedSAF shifts prototype alignment in heterogeneous federated learning from coordinate matching to inter-class structural relations and reports up to 3.52% gains over prior methods.
Heterogeneous feder- ated learning: State-of-the-art and research challenges
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
VARS-FL builds client reputation from validation loss reduction signals and uses sliding-window averaging plus log-scaled participation to select clients, yielding up to 36% faster convergence to 80% accuracy on non-IID Edge-IIoTset intrusion detection.
citing papers explorer
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From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
FedSAF shifts prototype alignment in heterogeneous federated learning from coordinate matching to inter-class structural relations and reports up to 3.52% gains over prior methods.
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VARS-FL: Validation-Aligned Client Selection for Non-IID Federated Learning in IoT Systems
VARS-FL builds client reputation from validation loss reduction signals and uses sliding-window averaging plus log-scaled participation to select clients, yielding up to 36% faster convergence to 80% accuracy on non-IID Edge-IIoTset intrusion detection.