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About Us - fedml.ai
FedML, Inc. (https://fedml.ai) is to help people or organizations have AI capability from data anywhere at any scale. FedML stands for “Fundamental Ecosystem Development/Design for Machine Learning” in a broad scope, and “Federated Machine Learning” in a specific scope. At the current stage, FedML is developing and maintaining a machine learning platform that …
Fedml.aiAbout - FedML
Visitors will want to know who is on the other side of the page. Use this space to write about yourself, your site, your business, or anything you want. Use the testimonials below to quote others, talking about the same thing – in their own words. …
Fedml.aiCareers - fedml.ai
Job Openings. FedML, Inc. is hiring for multiple positions in engineering, product, and business development (please click each position for the Job Description).If you are interested, please email [email protected] with CV attached, briefly introduce your background, and explain why you would like to work with us (suggested email title: “Apply FedML – Position” for easier …
Fedml.aiAI Applications - fedml.ai
Enabling Diverse AI Applications FedML Ecosystem facilitates federated learning research and productization in diverse application domains. With the foundational support from FedML Core Framework, it supports FedNLP (Natural Language Processing), FedCV (Computer Vision), FedGraphNN (Graph Neural Networks), and FedIoT (Internet of Things).
Fedml.aiempower AI from data anywhere at any scale - fedml.ai
Empower AI from data anywhere at any scale. FedML is to help people or organizations have AI capability from data anywhere at any scale.
Fedml.aiGitHub - FedML-AI/FedML: A Research-oriented …
2020-11-27 · A Research-oriented Federated Learning Library. Supporting distributed computing, mobile/IoT on-device training, and standalone simulation. Best Paper Award at NeurIPS 2020 Federated Learning works...
Github.comFedML Inc. - GitHub
FedGraphNN Public. A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops. Python 145 32. FedCV Public. FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks. Python 49 20.
Github.comGitHub - FedML-AI/FedGraphNN: A Research-oriented ...
2021-09-28 · A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops. - GitHub - FedML-AI/FedGraphNN: A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys …
Github.comGitHub - FedML-AI/FedNLP: FedNLP: A Research Platform for ...
It uses FedML repository as the git submodule. In other words, FedNLP only focuses on adavanced models and dataset, while FedML supports various federated optimizers (e.g., FedAvg) and platforms (Distributed Computing, IoT/Mobile, Standalone). The figure below is the overall structure of FedNLP. Installation
Github.comGitHub - FedML-AI/FedIoT: Federated Learning for Internet ...
2021-11-03 · Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection - GitHub - FedML-AI/FedIoT: Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection
Github.comFedML
FedML ... FedML
Open.fedml.aiGitHub - FedML-AI/FedCV: FedCV: A Federated Learning ...
2021-05-04 · FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks - GitHub - FedML-AI/FedCV: FedCV: A Federated Learning Framework for …
Github.comFedML/CI-install.sh at master · FedML-AI/FedML · GitHub
2021-09-21 · FedML/CI-install.sh. Go to file. Go to file T. Go to line L. Copy path. Copy permalink. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. nashirj Add missing dependency to CI-install.sh. Latest commit 3b85ecd on Sep 21, 2021 History.
Github.comOverview ‹ FedML: A Research Library and Benchmark for ...
In this work, we introduce FedML, an open research library and benchmark that facilitates the development of new 'federated learning algorithms' and fair performance comparisons. FedML supports three computing paradigms (distributed training, mobile on-device training, and standalone simulation) for users to conduct experiments in different ...
Media.mit.eduFedML: A Research Library and Benchmark for Federated ...
2020-12-12 · FedML supports three computing paradigms: on-device training using a federation of edge devices, distributed training in the cloud that supports exchanging of auxiliary information beyond just gradients, and single-machine simulation of a federated learning algorithm. FedML also promotes diverse algorithmic research with flexible and generic API design and …
Media.mit.eduTest and Deploy Your Code with Confidence - Travis CI
2021-06-15 · FedML-AI/FedML ©Travis CI, GmbH Rigaer Straße 8 10247 Berlin, Germany Work with Travis CI Blog Email Twitter Help Documentation Community Changelog Travis CI vs Jenkins Company Imprint Legal Travis CI Status Travis CI Status : All Systems ...
Travis-ci.orgFedML-AI · GitHub
FedML: A Research Library and Benchmark for Federated Machine Learning - FedML-AI
Dwf.lifeFedML: A Research Library and Benchmark for Federated ...
The source code, documents, and user community at https://fedml.ai. 1 Introduction Federated learning (FL) is a distributed learning paradigm that aims to train machine learning models from scattered and isolated data [1]. FL differs from data center-based distributed training in three major aspects: 1) statistical heterogeneity, 2) system constraints, and 3) trustworthiness. …
Chaoyanghe.com(PDF) FedML: A Research Library and Benchmark for ...
2020-07-27 · the source code, documents, and user community at https://FedML.ai. 1 Introduction. Federated learning (FL) is a distributed learning paradigm that aims to train machine learning . models from ...
Researchgate.netA Live Tutorial of FedML.ai MLOps Platform (open.fedml.ai ...
In this live demo, we invited four FedML.ai users to train a model collaboratively with a simplified workflow by our MLOps platform.
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