federated learning (FL) is a novel machine learning setting that enables
on-device intelligence via decentralized training and federated optimization.
Deep neural networks' rapid development facilitates the learn
Federated Learning aims to train a global model by utilizing decentralized data, but the highly dynamic networks of edge devices can cause delays and degrade the efficiency of the training process. To address this, DynamicFL is proposed as a novel framework that considers communication dynamics, data quality, and client selection strategies to improve system performance and achieve better model accuracy.