Statistics for topic federated-learning

Flower: A Friendly Federated AI Framework

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)

An Industrial Grade Federated Learning Framework

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.

All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.

Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)

An experimental platform for federated learning.

All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)

Flower: A Friendly Federated AI Framework

A curated list of references for MLOps

An Industrial Grade Federated Learning Framework

37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 24 datasets. www.pfllib.com/

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)

A curated list of advancements in Vertical Federated Learning, frameworks and libraries.

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.

A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. TPAMI, 2024.

CVPR 2024 accepted paper, An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning

You only need to configure one file to support model heterogeneity. Consistent GPU memory usage for single or multiple clients.

Flower: A Friendly Federated AI Framework

37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 24 datasets. www.pfllib.com/

A curated list of references for MLOps

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)

A curated list of advancements in Vertical Federated Learning, frameworks and libraries.

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.

✨✨A curated list of latest advances on Foundation Models with Federated Learning

CVPR 2024 accepted paper, An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning

FedML for Autonomous Driving (AD), Intelligent Transportation Systems (ITS), Connected and Automated Vehicles (CAV)

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.

HeFlwr: Federated Learning for Heterogeneous Devices

✨✨A curated list of latest advances on Foundation Models with Federated Learning

CVPR 2024 accepted paper, An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning

A curated list of advancements in Vertical Federated Learning, frameworks and libraries.

Flower: A Friendly Federated AI Framework

A curated list of references for MLOps

37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 24 datasets. www.pfllib.com/

Perform data science on data that remains in someone else's server

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)

A Git-like Version Control File System for AI & Data Product Management.

Federated learning framework made by researchers for researchers :)

You only need to configure one file to support model heterogeneity. Consistent GPU memory usage for single or multiple clients.

Federated learning with homomorphic encryption enables multiple parties to securely co-train artificial intelligence models in pathology and radiology, reaching state-of-the-art performance with priva...

A flexible, modular, and easy to use library to facilitate federated learning research and development in healthcare settings