In light of emerging legal requirements and policies focused on privacy protection, there is a growing trend of companies across various industries adopting Federated Learning (FL). This decentralized approach involves multiple clients or silos, collaboratively training a global model under the coordination 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 impact associated with Cross-Silo FL remains poorly understood due to the limited research in this area. This study seeks to bridge this gap by evaluating the sustainability of Cross-Silo FL throughout the entire AI product lifecycle, extending the analysis beyond the model training phase alone. We systematically compare this decentralized method with traditional centralized approaches and present a robust quantitative framework for assessing the costs and CO2 emissions in real-world Cross-Silo FL environments. Our findings indicate that the energy consumption and costs of model training are comparable between Cross-Silo Federated Learning and Centralized Learning. However, the additional data transfer and storage requirements inherent in Centralized Learning can result in significant, often overlooked CO2 emissions. Moreover, we introduce an innovative data and application management system that integrates Cross-Silo FL and analytics, aiming at improving the sustainability and economic efficiency of IT enterprises.

翻译：鉴于新兴的隐私保护法律要求和政策，各行业公司采用联邦学习（FL）的趋势日益增长。这种去中心化方法涉及多个客户端或数据孤岛，在中央服务器的协调下利用各自的私有本地数据协作训练全局模型。与需要数据共享和传输的传统方法不同，跨孤岛FL允许客户端共享模型更新而非原始数据，从而增强隐私性。尽管其采用率不断提高，但由于该领域研究有限，与跨孤岛FL相关的碳影响仍鲜为人知。本研究旨在通过评估跨孤岛FL在整个AI产品全生命周期中的可持续性来弥合这一差距，将分析范围扩展到模型训练阶段之外。我们系统比较了这种去中心化方法与传统的集中式方法，并提出了一个稳健的定量框架，用于评估实际跨孤岛FL环境中的成本和CO2排放。我们的研究结果表明，跨孤岛联邦学习与集中式学习在模型训练的能耗和成本方面具有可比性。然而，集中式学习固有的额外数据传输和存储需求可能导致大量且常被忽视的CO2排放。此外，我们引入了一种创新的数据与应用管理系统，该系统整合了跨孤岛FL与分析功能，旨在提升IT企业的可持续性和经济效益。