FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud

TL;DR

FedCostAware is a scheduling algorithm that optimizes cost-efficiency in federated learning on cloud spot instances, reducing expenses while maintaining model performance.

Contribution

It introduces a novel cost-aware scheduling method for federated learning on cloud spot instances, addressing resource management and cost reduction challenges.

Findings

Significantly reduces cloud costs compared to traditional schemes

Improves resource utilization and minimizes idle time

Enhances accessibility of federated learning in resource-constrained settings

Abstract

Federated learning (FL) is a distributed machine learning (ML) approach that allows multiple clients to collaboratively train ML models without exchanging original training data, offering a solution that is particularly valuable in sensitive domains such as biomedicine. However, training robust FL models often requires substantial computing resources from participating clients, which may not be readily available at institutions such as hospitals. While cloud platforms offer on-demand access to such resources, their usage can incur significant costs, particularly in distributed training scenarios where poor coordination strategies can lead to substantial resource wastage. To address this, we introduce FedCostAware, a cost-aware scheduling algorithm designed to optimize synchronous FL on cloud spot instances. FedCostAware addresses the challenges of training on spot instances and different client budgets by employing intelligent management of the lifecycle of spot instances. This approach minimizes resource idle time and overall expenses. Comprehensive experiments across multiple datasets demonstrate that FedCostAware significantly reduces cloud computing costs compared to conventional spot and on-demand schemes, enhancing the accessibility and affordability of FL.