Trending repositories for topic federated-learning
🎓 Automatically Update Some Fields Papers Daily using Github Actions (Update Every 12th hours)
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
Perform data science on data that remains in someone else's server
An open source FL implement with dataset(Femnist, Shakespeare, MNIST, Cifar-10 and Fashion-Mnist) using pytorch
A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on a...
🎓 Automatically Update Some Fields Papers Daily using Github Actions (Update Every 12th hours)
An open source FL implement with dataset(Femnist, Shakespeare, MNIST, Cifar-10 and Fashion-Mnist) using pytorch
A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on a...
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 unified framework for privacy-preserving data analysis and machine learning
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.
[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification
Perform data science on data that remains in someone else's server
🎓 Automatically Update Some Fields Papers Daily using Github Actions (Update Every 12th hours)
Selective Aggregation for Low-Rank Adaptation in Federated Learning [ICLR 2025]
An open source FL implement with dataset(Femnist, Shakespeare, MNIST, Cifar-10 and Fashion-Mnist) using pytorch
All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on a...
Kuscia(Kubernetes-based Secure Collaborative InfrA) is a K8s-based privacy-preserving computing task orchestration framework.
✨✨A curated list of latest advances on Large Foundation Models with Federated Learning
联邦学习模块化框架，支持各类FL。A universal federated learning framework, free to switch thread and process modes
Selective Aggregation for Low-Rank Adaptation in Federated Learning [ICLR 2025]
FedML for Autonomous Driving (AD), Intelligent Transportation Systems (ITS), Connected and Automated Vehicles (CAV)
🎓 Automatically Update Some Fields Papers Daily using Github Actions (Update Every 12th hours)
An open source FL implement with dataset(Femnist, Shakespeare, MNIST, Cifar-10 and Fashion-Mnist) using pytorch
Kuscia(Kubernetes-based Secure Collaborative InfrA) is a K8s-based privacy-preserving computing task orchestration framework.
AAAI 2024 accepted paper, FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
✨✨A curated list of latest advances on Large Foundation Models with Federated Learning
PyTorch Implementation of Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.
Source code for the paper "Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks".
联邦学习模块化框架，支持各类FL。A universal federated learning framework, free to switch thread and process modes
AAAI 2023 accepted paper, FedALA: Adaptive Local Aggregation for Personalized Federated Learning
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
You only need to configure one file to support model heterogeneity. Consistent GPU memory usage for single or multiple clients.
A unified framework for privacy-preserving data analysis and machine learning
Shepherd: A foundational framework enabling federated instruction tuning for large language models
A unified framework for privacy-preserving data analysis and machine learning
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on a...
Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.
Source code for the paper "Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks".
[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification
Perform data science on data that remains in someone else's server
A PyTorch Implementation of Federated Learning http://doi.org/10.5281/zenodo.4321561
Kuscia(Kubernetes-based Secure Collaborative InfrA) is a K8s-based privacy-preserving computing task orchestration framework.
🎓 Automatically Update Some Fields Papers Daily using Github Actions (Update Every 12th hours)
✨✨A curated list of latest advances on Large Foundation Models with Federated Learning
Selective Aggregation for Low-Rank Adaptation in Federated Learning [ICLR 2025]
Source code for the paper "Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks".
Kuscia(Kubernetes-based Secure Collaborative InfrA) is a K8s-based privacy-preserving computing task orchestration framework.
✨✨A curated list of latest advances on Large Foundation Models with Federated Learning
Source code for the paper "Group-based Federated Learning with Cost-efficient Sampling Mechanism in Mobile Edge Computing Networks".
AAAI 2024 accepted paper, FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
🎓 Automatically Update Some Fields Papers Daily using Github Actions (Update Every 12th hours)
You only need to configure one file to support model heterogeneity. Consistent GPU memory usage for single or multiple clients.
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.
🔬 FedCom为SWPU2022届本科毕业设计《基于社区检测的多任务聚类联邦学习》。本研究提出了一种多任务聚类联邦学习（clustered federated learning, CFL）的新方法，该方法的特点是基于社区检测（community detection）来进行聚类簇的动态划分。
Implementing the algorithm from our paper: "A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning".
FedML for Autonomous Driving (AD), Intelligent Transportation Systems (ITS), Connected and Automated Vehicles (CAV)
A unified framework for privacy-preserving data analysis and machine learning
Implementation of SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. TPAMI, 2024.
PyTorch Implementation of Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.
✨✨A curated list of latest advances on Large Foundation Models with Federated Learning
Source code for the paper "Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks".
Source code for the paper "Group-based Federated Learning with Cost-efficient Sampling Mechanism in Mobile Edge Computing Networks".
Selective Aggregation for Low-Rank Adaptation in Federated Learning [ICLR 2025]
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)
Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.
A PyTorch Implementation of Federated Learning http://doi.org/10.5281/zenodo.4321561
A unified framework for privacy-preserving data analysis and machine learning
[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification
Benchmark of federated learning. Dedicated to the community. 🤗
Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data
Simulation framework for accelerating research in Private Federated Learning
Paper notes and code for differentially private machine learning
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. TPAMI, 2024.
Privacy-Preserving Computing Platform 由密码学专家团队打造的开源隐私计算平台，支持多方安全计算、联邦学习、隐私求交、匿踪查询等。
Source code for the paper "Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks".
Federated learning framework made by researchers for researchers :)
(CVPR 2024) Communication-Efficient Federated Learning with Accelerated Client Gradient
CVPR 2024 accepted paper, An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning
Source code for the paper "Group-based Federated Learning with Cost-efficient Sampling Mechanism in Mobile Edge Computing Networks".
Selective Aggregation for Low-Rank Adaptation in Federated Learning [ICLR 2025]
FedML for Autonomous Driving (AD), Intelligent Transportation Systems (ITS), Connected and Automated Vehicles (CAV)
AAAI 2024 accepted paper, FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
A curated list of advancements in Vertical Federated Learning, frameworks and libraries.
P2PFL is a decentralized federated learning library that enables federated learning on peer-to-peer networks using gossip protocols, making collaborative AI model training possible without reliance on...
You only need to configure one file to support model heterogeneity. Consistent GPU memory usage for single or multiple clients.
A flexible, modular, and easy to use library to facilitate federated learning research and development in healthcare settings
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...
Nerlnet is a framework for research and development of distributed machine learning models on IoT
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. TPAMI, 2024.
Material workbench for the master-level course CS-E4740 "Federated Learning"
🎓 Automatically Update Some Fields Papers Daily using Github Actions (Update Every 12th hours)
Simulation framework for accelerating research in Private Federated Learning