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.
Federated machine learning: Concept and applications
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Position paper claiming that distributed training across massive edge devices can overcome data depletion and centralized compute monopolies in LLM scaling.
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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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Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices
Position paper claiming that distributed training across massive edge devices can overcome data depletion and centralized compute monopolies in LLM scaling.