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.
A survey on security and privacy of federated learning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
CLAD is a clustered federated learning framework with a dual-mode architecture for joint anomaly detection and attack classification in IoT using labeled and unlabeled data.
A model-agnostic framework combining GPBACC with robust aggregation and group testing for privacy-preserving and verifiable distributed learning in federated and decentralized settings.
citing papers explorer
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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.
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CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification
CLAD is a clustered federated learning framework with a dual-mode architecture for joint anomaly detection and attack classification in IoT using labeled and unlabeled data.
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Privacy-Preserving and Verifiable Approximate Distributed Coded Computing
A model-agnostic framework combining GPBACC with robust aggregation and group testing for privacy-preserving and verifiable distributed learning in federated and decentralized settings.