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 a better comparison. However, this repo is not the official code for those methods...
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 ...
Federated learning is 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....
Being the Federated Learning recognized as one of the most promising and efficient distributed learning algorithms, we investigate its behavior under actual application conditions. In particular, despite FederatedLearning enables end-devices to train a shared machine learning model while keeping data ...
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...
Private data—such as the specific shape of a particular workpiece—would remain private, but essential basic principles would be exchanged to further improve the capabilities of all robots. This is referred to as "federated learning." Numerous tests at TU Wien have proven the sink-cleaning robot...
Table 3. Comparison between the area under receiver operating characteristic scores of PPFL (c, s) and other algorithms on two datasets ICU, intensive care unit; NSCLC, non-small cell lung cancer; PPFL, personalized progressive federated learning; HICU, heart intensive care unit; MICU, medical ...