FATE | BestHub

DataFunSummit

Nov 6, 2022 · Artificial Intelligence

Guangfa Group’s Federated Learning Exploration, Platform Construction, and the Book “Federated Learning Principles and Applications”

This article outlines Guangfa Group’s initiatives in privacy computing and federated learning, detailing the development of its federated learning platform, contributions to open‑source FATE, industry standards, various application scenarios such as joint statistics, precise marketing, risk control, cross‑domain verification, and introduces their newly published book on federated learning principles and applications.

Artificial IntelligenceBig DataFATE

0 likes · 23 min read

Guangfa Group’s Federated Learning Exploration, Platform Construction, and the Book “Federated Learning Principles and Applications”

DataFunTalk

Jun 4, 2020 · Artificial Intelligence

Exploring Federated Recommendation Algorithms and Their Applications

This article introduces the challenges of traditional centralized recommendation systems, explains the principles and implementations of federated recommendation algorithms—including vertical and horizontal federated matrix factorization and factorization machines—using WeBank’s open-source FATE platform, and discusses cloud services, practical use cases, and performance benefits.

AIFATEFederated Learning

0 likes · 13 min read

Exploring Federated Recommendation Algorithms and Their Applications

DataFunTalk

Sep 9, 2019 · Artificial Intelligence

Federated Learning: Background, Techniques, Applications, and the FATE Open‑Source Platform

This article presents a comprehensive overview of federated learning, covering its motivation, vertical and horizontal variants, privacy‑preserving technologies, real‑world use cases, and the industrial‑grade open‑source platform FATE that enables secure cross‑organization machine learning.

Data CollaborationFATEFederated Learning

0 likes · 16 min read

Federated Learning: Background, Techniques, Applications, and the FATE Open‑Source Platform