The essential difficulty of gradient-based bilevel optimization using implicit differentiation is
to estimate the inverse Hessian vector product with respect to neural network parameters …

Federated optimization or federated learning (FL) involves optimization of the global model
or the server model by minimizing the global loss function which is weighted average of all …

In federated learning (FL), the significant communication overhead due to the slow
convergence speed of training the global model poses a great challenge. Specifically, a …

Federated optimization or Federated learning (FL) is a novel decentralized machine
learning algorithm, where a server model or global model is trained collaboratively without …

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 …

High communication overhead is a challenging problem in Federated Learning (FL), which
needs to be minimized. One way to reduce the overall communication overhead is directed …

Newton method of optimization is very much useful in machine learning and deep learning
optimizations due its order two convergence. The major problems of Newton method of …