Inertia-weighted FedAvg plus RoCoF-augmented ChebyKAN controllers achieve 75% generalization on unseen IEEE-39 faults and beat centralized PFL on two of three stabilized cases at full decentralization.
Towards personalized federated learning
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PrivFusion deploys agents to cluster semantically similar features and iteratively recommend transformations for harmonizing heterogeneous structured datasets in a privacy-preserving manner, evaluated on four COVID-19 datasets.
Fed-BAC uses contextual bandits and Thompson Sampling with additive clustering to deliver up to 35.5 percentage point accuracy gains and 1.5-4.8x faster convergence in hierarchical federated learning on non-IID data.
citing papers explorer
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Inertia-Informed Federated Learning Control Framework for Distributed Smart Grid Resilience
Inertia-weighted FedAvg plus RoCoF-augmented ChebyKAN controllers achieve 75% generalization on unseen IEEE-39 faults and beat centralized PFL on two of three stabilized cases at full decentralization.
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PrivFusion: A Privacy-preserving Multi-Agent Framework for Harmonizing Distributed Datasets
PrivFusion deploys agents to cluster semantically similar features and iteratively recommend transformations for harmonizing heterogeneous structured datasets in a privacy-preserving manner, evaluated on four COVID-19 datasets.
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Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning
Fed-BAC uses contextual bandits and Thompson Sampling with additive clustering to deliver up to 35.5 percentage point accuracy gains and 1.5-4.8x faster convergence in hierarchical federated learning on non-IID data.