The majoy version of FATE is upgraded! Seven highlights of FATE v1.1
Thanks for all your support. Our project FATE has reached the milestone of 1000 GitHub stars.
Besides, a lot of contributors from Tencent and other well-known enterprises, some from HKU and other universities, join into our open-source community. FATE will be better because of you.
Recently, version 1.1 of FATE is released officially. This new version provides a general algorithm framework and supports multiple federated learning algorithms. In addition, cooperated with Vmware, KubeFATE which is in partnership with Vmware provides fully containerized cloud native deployment. Now, the project has been launched on GitHub: (https://github.com/FederatedAI/FATE).
Introducing the several highlights of the updated version:
1. FATE v1.1 provides a general algorithm framework supporting secure aggregation for homogeneous federated learning. We encapsulate the main process of homogeneous federated learning. The developers can be easy to implement their own homogeneous federated learning algorithm. For example, we use the framework to implement the homogeneous neural network algorithm.
2. In this version, we add the Homogeneous Deep Neural Network, Heterogeneous Linear Regression and Heterogeneous Poisson Regression. It is very useful to use Heterogeneous Linear Regression in the scenario of predicting a continuous label. In addition, building models and forecasting in Heterogeneous Poisson Regression can help developers better to forecast the counts and frequency, such as the frequency of purchasing Insurance and the prediction of the accidents’ frequency.
3. As a significant version, FATE has multiple algorithms and supports multi-host heterogeneous federated modeling, covering the binary classification, multiple classification, Regression. It can enable multiple data providers to train the federated model under a heterogeneous scenario.
4. You can’t miss the FATE v1.1 if the developers already have the Spark clusters and they want to reuse preemptive resources. In this version, you can choose Spark as a computing engine to configure flexibly according to your actual situation. This is also an attempt to connect the Spark ecosystem.
5. In the scenario of the online Inference, FATE now adds the heterogeneous SecureBoost to forecast. Meanwhile, FATE-Serving also adds service governance function. In other words, it will deploy multiple identical servers online. If you use the service governance function, the process will switch between the remaining servers when one server has problems. This function can prevent the data lost and other situations due to the failure of one server.
6. At last, we are going to introduce KubeFATE which is in partnership with Vmware. In this version, KubeFATE provides fully containerized cloud native deployment for the developers. We encapsulate all components of FATE in containers and use Docker Compose or Kubernetes (Helm Charts) to deploy. The developers can easily deploy and use the FATE project by KubeFATE in the public cloud and private cloud.
Of course, we are much more than this. Let’s check out the poster to know more about the new functions of FATE v1.1.
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