  Federated Learning is a machine learning paradigm where we aim to train
machine learning models in a distributed fashion. Many clients/edge devices
collaborate with each other to train a single model on the central. Clients do
not share their own datasets with each other, decoupling computation and data
on the same device. In this paper, we propose yet another adaptive federated
optimization method and some other ideas in the field of federated learning. We
also perform experiments using these methods and showcase the improvement in
the overall performance of federated learning.
