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In light of emerging legal requirements and policies focused on privacyprotection, there is a growing trend of companies across various industriesadopting Federated Learning (FL). This decentralized approach involves multipleclients or silos, collaboratively training a global model under thecoordination of a central server while utilizing their private local data.Unlike traditional methods that necessitate data sharing and transmission,Cross-Silo FL allows clients to share model updates rather than raw data,thereby enhancing privacy. Despite its growing adoption, the carbon impactassociated with Cross-Silo FL remains poorly understood due to the limitedresearch in this area. This study seeks to bridge this gap by evaluating thesustainability of Cross-Silo FL throughout the entire AI product lifecycle,extending the analysis beyond the model training phase alone. We systematicallycompare this decentralized method with traditional centralized approaches andpresent a robust quantitative framework for assessing the costs and CO2emissions in real-world Cross-Silo FL environments. Our findings indicate thatthe energy consumption and costs of model training are comparable betweenCross-Silo Federated Learning and Centralized Learning. However, the additionaldata transfer and storage requirements inherent in Centralized Learning canresult in significant, often overlooked CO2 emissions. Moreover, we introducean innovative data and application management system that integrates Cross-SiloFL and analytics, aiming at improving the sustainability and economicefficiency of IT enterprises.

Holistic analysis on the sustainability of Federated Learning across AI product lifecycle