133 skills found · Page 4 of 5
sands-lab / SplitbudSplitBud is a Split Learning framework built upon Flower
ariadneac / RosenpyRosenPy is a complex-valued neural network library, written in Python; Incorporates CVNNs such as CV-FFNN (complex-valued feedforward neural network), SC-FFNN (split-complex feedforward neural network), CV-RBFNN (com-plex-valued radial basis function neural network), FC-RBFNN (fully-complex radial basis function neural network), and Deep PT-RBFNN (deep phase transmittance radial basis function neural network); and It enables the incorporation of properties intrinsic to neural networks, such as momentum, L2 regularization, early stopping, mini-batch, and learning rate decay.
SyedUmaidAhmed / Federated And Split Learning On EdgeThis is the official implementation of Federated and Split Learning on multiple Raspberry Pi's. It is the demonstration of training the data on edge devices without having threat to security issues.
zhxchd / SDAR SplitNNCode for NDSS '25 paper "Passive Inference Attacks on Split Learning via Adversarial Regularization"
vitorpamplona / SplitlearningSimple Python Socket-based Split Learning technique using PyTorch
ege-erdogan / SplitguardSupplementary code for the paper "SplitGuard: Detecting and MitigatingTraining-Hijacking Attacks in Split Learning"
Nokia-Bell-Labs / Data Centric Federated LearningEnhancing Efficiency in Multidevice Federated Learning through Data Selection
SharifAbuadbba / Split Learning 1DData61' CSIRO Distributed System Security Group. We have developed this algorithm to explore the question - Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training? The work has been accepted in asiaccs 2020
bharatadk / Python Splitter📁 Repo for python_splitter Python package. This package can split Images into Train, Test, Validation folders automatically by shuffling media/images for machine learning.
dixiyao / Patch Shuffling TransformerThe official implementatioin code of ICDM 2022 paper: "Privacy-Preserving Split Learning via Patch Shuffling over Transformers"
amarjeetsingh92 / YouTube Playlist Study PlannerThe YouTube Playlist Study Planner helps learners split any playlist into a day-wise study schedule. It fetches video titles and durations, balances total time across chosen days, and displays a structured plan with download options. Built with Python, yt-dlp, and Streamlit for easy, organized learning.
khoaguin / HESplitNetTwo-party Privacy-preserving Neural Network Training using Split Learning and Homomorphic Encryption (CKKS Scheme)
obadx / Recitations SegmenterA Machine Learning Model that split a recitations on pauses (وقف) not by Ayah
arnobt78 / Restaurant Food Ordering Management System React MERN FullStackA fullstack food ordering platform built with the MERN stack (MongoDB, Express.js, React, Node.js). It provides a modern, scalable, production-ready solution for online food ordering, restaurant management, real-time order tracking. The project is split into a robust backend REST API, feature-rich, responsive frontend SPA, ease of learning or reuse
mnswdhw / PFSLRepository implementing the lightweight split learning framework enabling edge devices to collaboratively train machine learning models with Data & Label Privacy.
intelligolabs / MTL SplitOfficial implementation of the paper "MTL-Split: Multi-Task Learning for Edge Devices using Split Computing" accepted @ DAC 2024.
AsifIqbal8739 / SplitLoadForecastingThis repository contains the experimental code for the "Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting" paper
Bribak / SURFY2This repository constitutes SURFY2 and corresponds to the bioRxiv preprint 'Updating the in silico human surfaceome with meta-ensemble learning and feature engineering' by Daniel Bojar. SURFY2 is a machine learning classifier to predict whether a human transmembrane protein is located at the surface of a cell (the plasma membrane) or in one of the intracellular membranes based on the sequence characteristics of the protein. Making use of the data described in the recent publication from Bausch-Fluck et al. (https://doi.org/10.1073/pnas.1808790115), SURFY2 considerably improves on their reported classifier SURFY in terms of accuracy (95.5%), precision (94.3%), recall (97.6%) and area under ROC curve (0.954) when using a test set never seen by the classifier before. SURFY2 consists of a layer of 12 base estimators generating 24 new engineered features (class probabilities for both classes) which are appended to the original 253 features. Then, a soft voting classifier with three optimized base estimators (Random Forest, Gradient Boosting and Logistic Regression) and optimized voting weights is trained on this expanded dataset, resulting in the final prediction. The motivation of SURFY2 is to provide an updated and better version of the in silico human surfaceome to facilitate research and drug development on human surface-exposed transmembrane proteins. Additionally, SURFY2 enabled insights into biological properties of these proteins and generated several new hypotheses / ideas for experiments. The workflow is as following: 1) dataPrep Gets training data from data.xlsx, labels it according to surface class and outputs 'train_data.csv' 2) split Gets train_data.csv, splits it into training, validation and test data and outputs 'train.csv', 'val.csv', 'test.csv'. 3) main_val Was used for optimizing hyperparameters of base estimators and estimators & weights of voting classifier. Stores all estimators. Evaluates meta-ensemble classifier SURFY2 on validation set. 4) classifier_selection All base estimators and meta-ensemble approaches are tested on the initial dataset as well as the expanded dataset including the engineered features and compared in terms of their cross-validation score. 5) main_test Evaluates SURFY2 on the separate test set (trained on training + validation set). 6) testing_SURFY Evaluates the original SURFY through cross-validation and on validation as well as test set. 7) pred_unlabeled Uses SURFY2 to predict the surface label (+ prediction score) for unlabeled proteins in data.xlsx. Also gets the feature importances of the voting classifier estimators. 8) getting_discrepancies Compare predictions with those made by SURFY ('surfy.xlsx') and store mismatches. Also store the 10 most confident mismatches (by SURFY2 classification score) from each class. 9) feature_importances Plot the 10 most important features for the voting classifier estimators (Random Forest, Gradient Boosting, Logistic Regression) to interpret predictions. 10) base_estimator_importances Plot the 10 most important features for the two most important base estimators (XGBClassifier and Gradient Boosting). 11) comparing_mismatches Separate datasets into shared & discrepant predictions (between SURFY and SURFY2). Compare feature means and select features with the highest class feature mean differences between prediction datasets. Statistically analyze differences in features means between classes in both prediction datasets. Plot 9 representative features with their means grouped according to class and prediction dataset to rationalize discrepant predictions. 12) tSNE_surfy2 Perform nonlinear dimensionality reduction using t-SNE on proteins with predictions from both SURFY and SURFY2. Plot the two t-SNE dimensions and label the proteins according to their prediction class in order to see where discrepant predictions reside in the landscape. Plot surface proteins with most prevalent annotated functional subclasses and label them according to their subclass to enable comparison to class predictions. Functional annotations came from 'surfy.xlsx'.
gggangmin / SplitFedHierarchical Federated Learning with model split
YodaEmbedding / Collaborative Intelligence[NeurIPS 2019] Mobile-cloud split deep learning model inference
