FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud

Sinha, Aditya; Li, Zilinghan; Liu, Tingkai; Kindratenko, Volodymyr; Kim, Kibaek; Madduri, Ravi

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https://hdl.handle.net/2142/130324 Copy

Description

Title

FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud

Author(s)

• Sinha, Aditya
• Li, Zilinghan
• Liu, Tingkai
• Kindratenko, Volodymyr
• Kim, Kibaek
• Madduri, Ravi

Issue Date

2025-09-17

Keyword(s)

• Federated learning
• Distributed computing
• Cloud computing
• Cost optimization
• Scheduling algorithm
• Spot instances
• Budget constraints

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.

Publisher

Allerton Conference on Communication, Control, and Computing

Series/Report Name or Number

2025 61st Allerton Conference on Communication, Control, and Computing Proceedings

ISSN

2836-4503

Type of Resource

Text

Genre of Resource

Conference Paper/Presentation

Language

eng

Handle URL

https://hdl.handle.net/2142/130324&&

Copyright and License Information

Copyright 2025 owned by the authors.

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