Demonstrator of Flower Federated Learning Tool: DEFT

基于ERC StG dial项目的最新成果，我们寻求开发联邦学习技术的商业概念验证。REDIAL项目的核心创新方向是联邦系统的可扩展模拟和有效训练，以及无监督视频理解和语音的实现方法。技术上的突破是机器学习系统的启用（例如，检测癌症、进行药物发现、检测金融欺诈），即使数据仍然留在原始机构，也可以对其进行训练，并且可以建立一个模型，从跨越所有这些位置的完整数据集中受益。通常由于法律、道德、知识产权、系统兼容性的困难，由于难以将数据放入单个数据中心，模型在数据集上的训练远远小于预期的数据集。建立隐私保护用户系统（如联合形式的数字助理、物联网设备和智能手机服务）的兴趣背后的第二个动机。这个商业机会来自于这样一个事实：REDIAL技术已经被集成到一个非常流行的完全开源的联邦学习工具包中，这个工具包叫做FLOWER——它也是在REDIAL的PI的实验室里开发的。基于REDIAL技术的FLOWER已经被下载超过42万次。围绕FLOWER的用户基础和社区在不断增长。但是开发联合解决方案对于主流应用来说仍然过于困难——公司需要更多的支持和易于使用的特定工具。这项概念验证赠款将促进一套强化的简单工具（我们称之为LEAP）的开发，并支持公司构建联邦学习应用程序。我们相信它们将出现在医疗保健、预防性维护、财务分析和药物发现等领域。

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.