Session Overview

Data is at the core of Machine Learning/Artificial Intelligence. Usually, model performance improves with more data available. Often, data an organization has is not sufficient and needs to be augmented with additional information. Nevertheless, there are data privacy concerns when it comes to cross-organization personal data sharing. There is an increasing awareness of staying compliant when sharing under regulations such as GDPR and PDPA. Even if it can be ensured that the personal data sharing stays compliant, there are plenty of business/commercial considerations that do not give enough justification/incentive for organizations to share data. Federated learning is a privacy-preserving machine learning technique that trains a model across multiple decentralized parties holding local data, without exchanging them.

AI Singapore has been working on building a system, named Synergos, to support Federated Learning. In this talk, we will present an overview of the key component of Synergos, and zoom into the core component which coordinates multiple parties to train a federated model.


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    AI Singapore's Journey into the World of Federated Learning

    • Abstract & Bio

    • AI Singapore's Journey into the World of Federated Learning


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