# FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud

arXiv: https://arxiv.org/abs/2505.21727 · HTML: https://mlsanity.com/paper/2505.21727/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2025.

## Built on by (most cited first, 2 of 2)

- [FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning](https://mlsanity.com/paper/2507.14322/) (2025)
- [Intelligent Cloud Orchestration: A Hybrid Predictive and Heuristic Framework for Cost Optimization](https://mlsanity.com/paper/2604.02131/) (2026)

## Abstract (arXiv)

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.