Towards more sustainable enterprise data and application management with
  cross silo Federated Learning and Analytics
Hongliu Cao
To comply with new legal requirements and policies committed to privacy
protection, more and more companies start to deploy cross-silo Federated
Learning at global scale, where several clients/silos collaboratively train a
global model under the coordination of a central server. Instead of data
sharing and transmission, clients train models using their private local data
and exchange model updates. However, there is little understanding of the
carbon emission impact of cross silo Federated Learning due to the lack of
related works. In this study, we first analyze the sustainability aspect of
cross-silo Federated Learning, across the AI product life cycle instead of
focusing only on the model training, with the comparison to the centralized
method. A more holistic quantitative cost and CO2 emission estimation method
for real world cross-silo Federated Learning setting is proposed. Secondly, we
propose a novel data and application management system using cross silo
Federated Learning and analytics to make IT companies more sustainable and cost
effective.