Statistics for topic federated-learning
RepositoryStats tracks 607,679 Github repositories, of these 212 are tagged with the federated-learning topic. The most common primary language for repositories using this topic is Python (141). Other languages include: Jupyter Notebook (21)
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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
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...
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...
Shepherd: A foundational framework enabling federated instruction tuning for large language models
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)
Perform data science on data that remains in someone else's server
Everything you want about DP-Based Federated Learning, including Papers and Code. (Mechanism: Laplace or Gaussian, Dataset: femnist, shakespeare, mnist, cifar-10 and fashion-mnist. )
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...
Everything you want about DP-Based Federated Learning, including Papers and Code. (Mechanism: Laplace or Gaussian, Dataset: femnist, shakespeare, mnist, cifar-10 and fashion-mnist. )
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.
Perform data science on data that remains in someone else's server
37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 24 datasets. www.pfllib.com/
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.
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...
Material workbench for the master-level course CS-E4740 "Federated Learning"
Programming Assignments and Quizzes from all courses within the Blockchain Specialization offered by The University at Buffalo and The State University of New York and key takeaways from study and re...
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
(CVPR 2024) Communication-Efficient Federated Learning with Accelerated Client Gradient
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 :)
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
You only need to configure one file to support model heterogeneity. Consistent GPU memory usage for single or multiple clients.