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To comply with new legal requirements and policies committed to privacyprotection, more and more companies start to deploy cross-silo FederatedLearning at global scale, where several clients/silos collaboratively train aglobal model under the coordination of a central server. Instead of datasharing and transmission, clients train models using their private local dataand exchange model updates. However, there is little understanding of thecarbon emission impact of cross silo Federated Learning due to the lack ofrelated works. In this study, we first analyze the sustainability aspect ofcross-silo Federated Learning, across the AI product life cycle instead offocusing only on the model training, with the comparison to the centralizedmethod. A more holistic quantitative cost and CO2 emission estimation methodfor real world cross-silo Federated Learning setting is proposed. Secondly, wepropose a novel data and application management system using cross siloFederated Learning and analytics to make IT companies more sustainable and costeffective.

Towards more sustainable enterprise data and application management with cross silo Federated Learning and Analytics