This repository is developed, mainly byMM. Kamani, based on our group's research on distributed and federated learning algorithms. We also developed the works of other groups' proposed methods using FedTorch for
Beyond federated learning: fusion strategies for diabetic retinopathy screening algorithms trained from different device typesPurpose :Diabetic Retinopathy (DR) is one of the most common causes of blindness, and a screening using fundus images helps to detect DR at early stage. Various types of ...
By running such algorithms on musical audio, learning labels are automatically computed, without the need for soliciting human annotations. Algorithmically computed outcomes will likely not be perfect and include noise or errors. At the same time, we consider them as a relatively efficient way to ...
When predicting mortality on non-small cell lung cancer data, PPFL (c, s) achieved the highest performance with AUROCs of 0.71 and 0.75 on MSK and TCGA datasets, when compared to the average values of 0.62 and 0.57 of the other algorithms, respectively, as shown inFigure S1. PPFL (c, ...
Federated learningis a technology that allows the training of high-quality models using data distributed across independent centers. Instead of consolidating data on a single central server, each center keeps its data secure, while the algorithms and predictive models move between them. ...
In addition to the reference model, we selected models from four different families of machine learning algorithms. First, we distinguished between single-target and multi-target models. Linear regres- sion (LR) belongs to the former class and requires a fairly low number of calculation steps. ...
Federated learning for double unbalance settings (sample quantities imbalance for different classes in client and label or class imbalance for different client cross-client) Framework of FedGR Part of Experiment Results (full results are listed in our paper) AlgorithmsCIFAR-10 (2)CIFAR-10 (3)CIFAR...
Types of Learning Given that the focus of the field of machine learning is “learning,” there are many types that you may encounter as a practitioner. Some types of learning describe whole subfields of study comprised of many different types of algorithms such as “supervised learning.” Others...
1.12. Multiclass and multioutput algorithms [Internet]. Scikit-Learn [cited 2022 Nov 16] Available from: https://scikit-learn/stable/modules/multiclass.html. Google Scholar Cited by (4) Large language models in critical care 2025, Journal of Intensive Medicine Show abstract Technologies for Inter...
The classification experiments are performed using the 1D Convolutional LSTM network and its performance is then compared with two baseline machine learning (ML) algorithms K-Nearest Neighbor and Random Forest. The experimental results show the variation of classification accuracies from one brain region...