FATE

FATE (Federated AI Technology Enabler) is an open source project of federated machine learning framework hosted by Linux Foundation.

[FATE @FateFedAI]

🚀Ready to unleash the power of federated learning for large language models? Learn how in our hands-on guide! Discover the step-by-step process of running #FATE-#LLM using #KubeFATE, our cloud native FATE solution, in this practical tutorial: https://t.co/nUnV4vuQiM

[FATE @FateFedAI]

Federated Learning now meets LLM! We’re super excited to announce FATE v1.11! This major release introduces FATE-LLM, a federated learning approach to support parameter-efficient tuning of Large Language Models. Check this & other enhancements out here: https://t.co/6XZz4OuEev

[FATE @FateFedAI]

And check out this project on GitHub and see how it can boost the operation and application of FATE. https://t.co/sa5PxgVTd2

[FATE @FateFedAI]

Extremely happy to welcome the donation of FedLCM from @vmwocto to the FATE and @LFAIDataFdn community. Using FedLCM, we can easily and securely create FATE federations, deploy FATE systems for multiple parties and start FML jobs, all by several clicks through its graphical UI!

[LF AI & Data Foundation @LFAIDataFdn]

VMware donates FedLCM to the FATE Project, building on federated learning at LF AI & Data. For more info, read here: https://t.co/79SgHzAI3A
@WMware @LFAIandDataFdn #AI #Data #FATE #FATEproject

[FATE @FateFedAI]

We are thrilled to announce the FATE & KubeFATE v1.9.0 release! Welcome to enjoy 20x faster PSI algorithm, HA and rolling upgrade, interconnection across computing engines, authn hooks & more for better security and other 40+ new features and improvements! https://t.co/qSsdRA72r9

[FATE @FateFedAI]

We are thrilled to announce the FATE & KubeFATE v1.9.0 release! Welcome to enjoy 20x faster PSI algorithm, HA and rolling upgrade, interconnection across computing engines, authn hooks & more for better security and other 40+ new features and improvements! https://t.co/qSsdRA72r9

[FATE @FateFedAI]

Do you know KubeFATE can support deploy FATE using docker alone? You don't need Kubernetes installation to have a FATE cluster running. See how to launch a FATE instance with a simple script: https://t.co/7aj1B39ER4

[FATE @FateFedAI]

Due to the nature of federated machine learning, multiple participants are involved to build a model. The inter-connection between all these parties can become unmanageable when the number grows rapidly. See how FATE can solve the problem.https://t.co/zXKkqchelf

[FATE @FateFedAI]

Want to get to know more about KubeFATE internals? KubeFATE is built around Helm but with another layer of abstraction to further enhance its flexibility. The juicy technical details of its implementation are addressed in this deep-dive: https://t.co/b8CFc4iYqp

[FATE @FateFedAI]

Spark cluster can be leverage by FATE too so that you don't need to set up it from scratch. See this how-to guide on working with such settings: https://t.co/vCjIvr035z

[FATE @FateFedAI]

Similar as using Spark, FATE can use Pulsar as its federation engine. Jiahao Chen, engineer at VMware's Office of CTO department, has presented a talk on FATE's integration with Pulsar on Pulsar Virtual Summit North America 2021. See the recap here: https://t.co/TMjb0ON2NR

[FATE @FateFedAI]

As an flexible framework, FATE can work with different types of computing, storage and federation engines. Here is a deep dive about FATE's architecture around them and how to use Spark to run federated learning jobs:
https://t.co/kUWakm1g6u

[FATE @FateFedAI]

Hi,

We are pleased to announce the release of FATE v1.8.0 and KubeFATE v1.8.0. You can see huge performance improvements (up to 5x faster), security enhancement, one-vs-rest HomoLR support, optimized scheduling and testing framework and more!

https://t.co/HVLrjBVzpl

[FATE @FateFedAI]

Jupyter Notebook integrated! FATE project contains a python SDK and a pipeline package that can be directly imported to launch FATE jobs via the Notebook. This is how to conveniently achieve that with the help of KubeFATE:
https://t.co/BgSqxw5CS6

[FATE @FateFedAI]

KubeFATE contains a log aggregation feature that users can view all or any components' log via the CLI interface:https://t.co/2vDJXRBV8r

[FATE @FateFedAI]

Introducing FATE (Federated AI Technology Enabler): An Industrial Grade Federated Learning Framework. Initiated by Webank's AI Department, it supports federated learning architectures and secure computation of various machine learning algorithms.
https://t.co/YBzCc2wx1O

[FATE @FateFedAI]

In this post, we will exploring the way we do federated learning and how to solve its complexities using cloud-native technologies. https://t.co/q4ENkzRiZV

[FATE @FateFedAI]

Managing your FATE installation in Kubernetes using the operator pattern? VMware has been working with Kubeflow community to realize that. It even includes managing your FATE training & inference jobs! Check out the FATE-operator here:https://t.co/nITTzTiGC5

[FATE @FateFedAI]

Hi,

We are pleased to announce the release of FATE 1.61 and KubeFATE 1.6.1. Based from 1.6.0, there are many improvements including new features and bug-fix. Welcome to check them out!

Major Features and Improvements in FATE v1.6.1
https://t.co/62tHW9dLPW

[FATE @FateFedAI]

Case-study: See how FATE can help in risk management of credit in small and micro enterprises. Spoiler alert - there is over 10+ percent in model performance enhancement thanks to the implementation of federated machine learning.https://t.co/t0AGon9g0V