# Federated Learning for Edge Computing: A Survey
## 4 Frameworks
### Other  
Other less-known frameworks and libraries are also designed for FL. XayNet [93] is Xayn's FL backend. This open-source framework is designed for mobile devices and web browsers to train models. Hive [94] is the name of a FL system that Ping An Technology plans to create, but we have not been able to find official documentation. The Substra project is a closed-source solution that is currently under development. Although strategic discussions have been about the potential for opening up newer versions, no decision has yet been made [95].  
Not all FL technologies are used in open-source frameworks. Leading companies have built proprietary libraries supplied under a restricted license and not open source. The NVIDIA Clara Train software development kit (SDK), which contains FL starting with version 2.0, illustrates such a library. NVIDIA Clara [96] is another framework that supports FL. It is a scalable computing platform. This platform was primarily designed for developers who create and deploy applications for medical, healthcare, and life science applications. The goal is to create intelligent devices and automate workflows. NVIDIA Clara provides libraries, SDKs, and reference applications for programmers, data scientists, and researchers. This framework's key contribution is GPU support and acceleration, which makes it possible to develop safe and expandable real-time applications. NVIDIA Clara supports several healthcare projects, such as Clara for Medical Devices, Clara for Drug Discovery, Clara for Smart Hospital, Clara for Medical Images, and Clara for Genomics. All these projects use artificial intelligence and NVIDIA Clara FL to improve the healthcare field. Several experiments use the framework mentioned earlier for FL. Most of these studies are focused on healthcare [97; 98], but there is also research and experiments that deal with IoT and industry [99]. NVIDIA Clara used machine learning libraries, including TensorFlow and Pytorch.  
IBM federated learning [100] focuses on data integration, customer privacy solutions, regulatory compliance, and big data across multiple locations. IBM FL is broadly configurable for various deployment scenarios, from mobile and edge scenarios and edge devices to multi-cloud environments in the enterprise to use cases that extend beyond the organization's boundaries. Data centers and cloud instances from various providersfrequently participate in FL, as do edge services with access to data from industrial facilities. IBM FL used a variety of machine learning libraries, including Keras, TensorFlow, Pytorch, SK Learn, and RLLib, which are compatible with IBM FL architecture [101]. Additionally, it offers APIs that can be used to create new algorithms for FL. IBM Federated Learning is independent of any particular machine learning library or paradigm. It can be used for deep neural networks or simpler algorithms such as decision trees or support vector machines. IBM provides two different versions of IBM: Federated Learning in IBM Cloud Pak for Data and IBM Federated Learning - Community Edition. Additionally, the IBM research team encourages fairness strategies for FL that lessen bias [102].