This Article Is Based On The Alibaba Research on FederatedScope. All Credit For This Research Goes To The Researchers 👏👏👏 Please Don't Forget To Join Our ML Subreddit
Federated learning is a machine learning technique that trains a model across multiple distributed nodes, or hosts, as the name suggests. Each node uses its own training data. If the model parameters are shared between the nodes instead of the raw data, the data can be kept private.
Obtaining training data to design and develop machine learning models is increasingly challenged due to privacy concerns, and federated learning can help solve some of these problems.
Chinese e-commerce giant Alibaba has created a federated learning platform that makes it possible to create machine learning algorithms without sharing training data.
The source code for FederatedScope has been released on GitHub under the Apache 2.0 license.
The platform is a comprehensive federated learning platform that enables flexible customization for many machine learning applications in academia and industry.
It also aims to be easy to use as users can integrate their own components such as datasets and models for specialized applications.
According to Alibaba, FederatedScope has an event-driven architecture and offers multiple tools, including a collection of benchmark datasets, well-known model architectures, example federated learning algorithms, and automatic tuning mechanisms.
Developers can leverage these capabilities to create and configure task-specific federated learning systems in computer vision, natural language processing, speech recognition, graph learning, and recommendation.
FederatedScope also offers data protection through differential privacy and multi-party computing to meet various data protection requirements.