Towards Heterogeneous Clients with Elastic Federated Learning

Understand

Federated learning involves training machine learning models over devices or data silos, such as edge processors or data warehouses, while keeping the data local.

• Training in heterogeneous and potentially massive networks introduces bias into the system, which is originated from the non-IID data and the low participation rate in reality.
• In this paper, we propose Elastic Federated Learning (EFL), an unbiased algorithm to tackle the heterogeneity in the system, which makes the most informative parameters less volatile during training, and utilizes the incomplete local updates.
• It is an efficient and effective algorithm that compresses both upstream and downstream communications.