<ul><li>The study focuses on evaluating the sustainability of Cross-Silo Federated Learning (FL) throughout the entire AI product lifecycle.</li><li>Cross-Silo FL is a decentralized approach that allows clients to share model updates rather than raw data, enhancing privacy.</li><li>The energy consumption and costs of model training are comparable between Cross-Silo FL and Centralized Learning.</li><li>Centralized Learning can result in significant CO2 emissions due to additional data transfer and storage requirements.</li></ul>

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

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