Federated Learning Papers with Code Federated Learning Papers This GitHub repository contains an updated list of Federated Learning papers as of May 19, 2025. The resources are collected from various sources, including arXiv, NeurIPS, ICML, ICLR, ACL, EMNLP, AAAI, IJCAI, KDD, CVPR, ICCV, ECCV...
PyTorch Implementation of Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach pytorchmeta-learningfederated-learningpersonalized-federated-learning UpdatedFeb 1, 2023 Python omarfoq/knn-per Star43 Code
Related Repositories (Projects) KubeFATE: An operational tool for the FATE platform using cloud native technologies such as containers and Kubernetes. FATE-Flow: A multi-party secure task scheduling platform for federated learning pipeline. FATE-Board: A suite of visualization tools to explore and und...
Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic...
What is the significance of federated learning in the context of multi-party computation? How does Multi Party Computation facilitate intra-group model aggregation? What are some notable open-source frameworks for federated learning? What risks are associated with sharing model parameters in federated ...
federated-learning matlab code 摘要：一、引言 1.联邦学习简介 2.联邦学习在 MATLAB 中的实现 二、联邦学习概述 1.联邦学习的概念 2.联邦学习的工作原理 3.联邦学习的主要优势 三、MATLAB 代码实现联邦学习 1.安装与配置 MATLAB 2.编写联邦学习模型 3.训练与仿真联邦学习模型 四、结论 1.MATLAB 在联邦学习中...
Federated learning, the approach of training models on locally kept data and only sharing the learned parameters, is a potential technique to alleviate data sharing obstacles. This systematic review investigates the use of federated learning within the food domain, structures included papers in a ...
Potential environmental impact of machine learning by large-scale wireless networks is a major challenge for the sustainability of future smart ecosystems. In this paper, we introduce sustainable machine learning in federated learning settings, using rechargeable devices that can collect energy from the ...
Fairness has emerged as a critical problem in federated learning (FL). In this work, we identify a cause of unfairness in FL -- conflicting gradients with large differences in the magnitudes. To address this issue, we propose the federated fair averaging (FedFV) algorithm to mitigate potential...
Although many studies have been proposed to address this challenge, we find that they fail to achieve high performance in image datasets with deep learning models. In this paper, we propose MOON: model-contrastive federated learning. MOON is a simple and effective federated learning framework. The...