结论(Conclusion)
Federated learning has received increasing attention in recent years, and for good reasons: a flexible paradigm for implementing machine learning in a distributed and privacy preserving way, FL has the potential to address some of the current limitations of applied artificial intelligence by facilitating the collaboration around data across an industry.
近年来,联合学习受到越来越多的关注,并且由于充分的原因:以分布式和隐私保护的方式实施机器学习的灵活范例,FL可以通过促进围绕以下方面的协作来解决应用人工智能的当前局限性整个行业的数据。
The growth of IoT provides another game changing application to FL, and technologies like blockchain can support the development of new business models to incentivise participants in a FL environment with new business models.
物联网的发展为FL提供了另一个改变游戏规则的应用程序,而区块链等技术可以支持新业务模型的开发,从而通过新业务模型激励FL环境中的参与者。
What are use cases for FL you can think of? We’d love to hear about those in the comments!
您能想到哪些FL用例?我们希望知道评论中的内容!
This article was written by Jean-Christophe Quillet, Senior AI & Machine Learning Technologist, Digital Catapult.
本文由Digital Catapult的高级AI和机器学习技术专家Jean-Christophe Quillet撰写。