Alibaba Introduces ‘FederatedScope’: An Easy-To-Use Federated Learning Platform Providing Comprehensive Functionalities

This Article Is Based On The Alibaba Research on FederatedScope. All Credit For This Research Goes To The Researchers 👏👏👏

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Federated learning is a machine learning technique that trains many models of dispersed nodes or hosts, as the name suggests. Each node utilizes its own training data. If the model parameters are shared between the nodes rather than the raw data, the data can be kept private.

Due to privacy concerns, acquiring training data, design and evolving machine learning models are some of the issues that are being questioned, and some of the issues that can be addressed are those of federated learning.

The Chinese e-commerce behemoth, Alibaba, has created a federated learning platform that enables machine learning algorithms to provide shared training data.

The source code for FederatedScope was released on GitHub under the Apache 2.0 license.

The platform is a comprehensive federated learning platform that provides flexible customization for many machine learning applications in academia and industry.

It’s also simple to use, with users being able to incorporate their own components such as datasets and models into specialized applications.

According to Alibaba, FederatedScope has an event-driven architecture and many tools, including a collection of benchmark datasets, well-known model architectures, sample federated learning algorithms, and automatic tuning mechanisms.

Developers can use 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 provides privacy protection through differential privacy and multi-party computing to suit various privacy requirements.





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