FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
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Updated
Sep 3, 2022
FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 20 datasets.
All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.
Personalized federated learning codebase for research
AAAI 2023 accepted paper, FedALA: Adaptive Local Aggregation for Personalized Federated Learning
A curated list of Federated Learning papers/articles and recent advancements.
PyTorch implementation of Federated Learning algorithms FedSGD, FedAvg, FedAvgM, FedIR, FedVC, FedProx and standard SGD, applied to visual classification. Client distributions are synthesized with arbitrary non-identicalness and imbalance (Dirichlet priors). Client systems can be arbitrarily heterogeneous. Several mobile-friendly models are prov…
Federated Neural Collaborative Filtering (FedNCF). Neural Collaborative Filtering utilizes the flexibility, complexity, and non-linearity of Neural Network to build a recommender system. Aim to federate this recommendation system.
This is official code for ACIIDS2022 paper "Meta-learning and Personalization layer in Federated learning"
FedCD: Improving Performance in non-IID Federated Learning
Implementation of Federated Learning using the Flower Framework
This is a Tensorflow implementation of the paper Homogeneous Learning: Self-Attention Decentralized Deep Learning.
A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures
Looking at optimizing the performance of Vision Transformers under a federated learning environment
Official implementation of paper "Brain Age Estimation Using Structural MRI: A Clustered Federated Learning Approach"
Federated Learning for Sentiment Analysis
Membership Inference Attacks on Imbalanced Federated Learning setups
Just a casual project that provides a minor solution to the distributed learning of block non-i.i.d. data
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