Sangyoung Park

Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning

Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a cloud server. Straggling clients have been a problem for FL as they introduce delays in aggregating the local models and hence, the convergence of the global model. Ther...