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FAGH: Accelerating Federated Learning with Approximated Global Hessian
Specifically, a large number of communication rounds are required to achieve the convergence in FL.
SOFIM: Stochastic Optimization Using Regularized Fisher Information Matrix
This paper introduces a new stochastic optimization method based on the regularized Fisher information matrix (FIM), named SOFIM, which can efficiently utilize the FIM to approximate the Hessian matrix for finding Newton's gradient update in large-scale stochastic optimization of machine learning models.
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