Federated learning enables multiple clients, such as mobile phones and organizations, to collaboratively learn a shared model for prediction while protecting local data privacy. However, most recent research and applications of federated learning assume that all clients have fully labeled data, which is...
Semi-supervised learningMulti-branch modelIn the vanilla federated learning (FL) framework, the central server distributes a globally unified model to each client and uses labeled samples for training. However, in most cases, clients are equipped with different devices and are exposed to a variety...
semi-decentralized federated edge learning (SD-FEEL), a unique FEEL architecture, was recently put up by [20]. Instead of focusing on the functionality of data storage and task distribution [21], the edge servers in this system perform edge aggregation throughout each training round, allowing ...
first FL framework, namely GraphFL, for semi-supervised node classification on graphs. Our framework is motivated by meta-learning methods. Specifically, we propose two GraphFL methods to respectively address the non-IID issue in graph data and handle the tasks with new label domains. Furthermore...
This is the official PyTorch implementation of CVPR 2022 paper "RSCFed: Random Sampling Consensus Federated Semi-supervised Learning". Preparation Create conda environment: conda create -n RSCFed python=3.8 conda activate RSCFed Install dependencies: ...
internet of medical things; federated learning; semi-supervised machine learning; multi-task learning; transfer learning1. Introduction With the ever-increasing availability of information, the connectivity among different electronic devices, and the transformation of the healthcare system, we have a new...
blockchain; semi-centralized framework; personalized federated learning; hypernetwork; non-iid data1. Introduction In recent years, the digital era has experienced continuous growth, leading to increased interactions among internet users. This surge in data generated by electronic devices presents ...
About A Federated Learning Approach for Non-IID Data Using Semi-Supervised DCGAN Resources Readme Activity Stars 0 stars Watchers 2 watching Forks 0 forks Report repository Releases No releases published Packages No packages published Languages Jupyter Notebook 100.0% Footer...
The recent improvement in automated cancer classification using deep learning methods has reached a human-level performance requiring a large amount of annotated data assembled in one location, yet, finding such conditions usually is not feasible. Recently, federated learning (FL) has been proposed to...
2023Federated learning allows multiple clients to jointly train a model on their private data without revealing their local data to a centralized server. Thereby, federated learning has attracted increasing attention in recent years, and many algorithms have been proposed. However, existing federated lea...