MazharAly

Deep-Learning-2023-t81

Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks

federated-double-deep-Q_network-

A framework that exploits the potentials of distributed federated learning and double deep Q-networks to minimize joint energy and delay in IoT networks

Federated-learning-based-prediction

use federated learning to predict building energy consumption

FedProx-PyTorch

PyTorch implementation of FedProx (Federated Optimization for Heterogeneous Networks, MLSys 2020).

MazharAly

Config files for my GitHub profile.

On-the-Convergence-of-Federated-Learning-Regression-on-Energy-Grid-Environment-

MazharAly/Federated-learning-based-prediction

use federated learning to predict building energy consumption

MazharAly/Deep-Learning-2023-t81

Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks

MazharAly/federated-double-deep-Q_network-

A framework that exploits the potentials of distributed federated learning and double deep Q-networks to minimize joint energy and delay in IoT networks

MazharAly/FedProx-PyTorch

PyTorch implementation of FedProx (Federated Optimization for Heterogeneous Networks, MLSys 2020).

MazharAly/MazharAly

Config files for my GitHub profile.

MazharAly/On-the-Convergence-of-Federated-Learning-Regression-on-Energy-Grid-Environment-