Federated Granger causality uncertainty reaches a steady state determined solely by aleatoric client data statistics, independent of epistemic priors, supporting reliable hypothesis testing for cross-client interactions.
Fedbe: Making bayesian model ensemble applicable to federated learning
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Q-LocalAdam reduces optimizer memory by 3.37x via tailored 8-bit quantization for Adam states while maintaining or improving accuracy under data heterogeneity in edge federated learning.
A survey organizing knowledge distillation techniques for addressing privacy, heterogeneity, communication, and personalization challenges in federated learning.
citing papers explorer
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Towards Uncertainty-Aware Federated Granger Causal Learning
Federated Granger causality uncertainty reaches a steady state determined solely by aleatoric client data statistics, independent of epistemic priors, supporting reliable hypothesis testing for cross-client interactions.
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Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning
Q-LocalAdam reduces optimizer memory by 3.37x via tailored 8-bit quantization for Adam states while maintaining or improving accuracy under data heterogeneity in edge federated learning.
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Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
A survey organizing knowledge distillation techniques for addressing privacy, heterogeneity, communication, and personalization challenges in federated learning.