FedGrad: Optimisation in Decentralised Machine Learning
This work addresses optimization challenges in federated learning for distributed machine learning systems, but it appears to be incremental.
The paper tackles the problem of optimizing federated learning by proposing an adaptive optimization method, resulting in improved overall performance as demonstrated through experiments.
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.