Federated Learning = Data Access + Privacy

  • “Federated Learning – Currently, data silos and privacy protection are two big challenges for AI. As an encrypted distributed machine learning framework, FL can tackle both problems by allowing different parties to build models collaboratively without the need to reveal their data. The method helps to advance AI modeling while protecting data and privacy.
  • Direct data merging will violate privacy regulations. FL is a compliance method strictly following laws and regulations, and is now used in fintech, healthcare, smart city, and other industrial applications.”
  • https://www.businesswire.com/news/home/20191218005275/en/NuerIPS-2019-China’s-WeBank-Mila-Tencent-Partner

    Professor Yoshua Bengio, A.M.

    Turing Award Winner

    Founder of Mila-Quebec Artificial Intelligence Institute

    One of the “three musketeers of deep learning”


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