Demonstrator of Flower Federated Learning Tool: DEFT

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Building upon recent results from the ERC StG REDIAL project, we seek to develop a commercial proof-of-concept of federated learning technology. Core innovation in the REDIAL project has been been made in the direction of scalable simulation and efficient training of federated systems -- along with enabling methods for unsupervised video understanding and speech. The technical breakthrough is the enabling of machine learning systems (e.g., detection of cancer, performing drug discovery, detecting financial fraud) that can be trained even if the data remains at the originating institution -- and yet a model can be built that benefits from the complete set of data that spans all of these locations. Often due to legal, ethical, intellectual property, difficulty of system compatibility -- models are trained on data sets far smaller than would be expected because of the difficulties of bring data into one single data center. A secondary motivation behind interest in the ability to build privacy preserving user systems, such as federated forms of digital assistants, IoT devices and smartphone services. The commercial opportunity extends from the fact REDIAL technology has been integrated within a highly popular completely open-source federated learning toolkit called FLOWER -- which has also developed in the lab of the PI of REDIAL. More than 420,000 downloads of FLOWER with REDIAL based technology have occurred. There is a growing user base and community around FLOWER. But developing federated solutions remains too hard for mainstream use -- and companies require more support, and easy to use specific tools. This proof-of-concept grant would be to facilitate the development of a hardened simple set of tools (we call it LEAP) and support for companies build federated learning applications. We believe they will come in the areas such as healthcare, preventative maintenance, financial analysis, and drug discovery.