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Privacy Preserving

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/albertlordsun/bhgnn-rt

공식 코드

BHGNN-RT: Network embedding for directed heterogeneous graphs

https://github.com/alelevente/felesh

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A V2X-based Privacy Preserving Federated Measuring and Learning System

https://github.com/alessiomora/puf_unlearning

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Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-Gradients

https://github.com/alexandor91/se3-equi-graph-registration

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Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration

https://github.com/alexmonti19/dagnet

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DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting

https://github.com/alexrsaff/gbds_project

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Predicting Citi Bike Demand Evolution Using Dynamic Graphs

https://github.com/alge24/ADA-UGNN

공식 코드

A Unified View on Graph Neural Networks as Graph Signal Denoising

https://github.com/alge24/DyGNN

공식 코드

Streaming Graph Neural Networks

https://github.com/algebraicdianuj/DC_U

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Dataset Condensation Driven Machine Unlearning

https://github.com/ali-forootani/asynchronous-federated-learning

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Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning

https://github.com/alibaba-edu/mpc4j

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OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization

https://github.com/alibaba/federatedscope

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On the Convergence of Zeroth-Order Federated Tuning for Large Language Models

https://github.com/aliosia/DeepPrivInf2017

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Privacy-Preserving Deep Inference for Rich User Data on The Cloud

https://github.com/alirezadizaji/graphroar

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On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions

https://github.com/alirezaghavamipour/pplr_ss

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Privacy-preserving Logistic Regression with Secret Sharing

https://github.com/allanchen95/gccad

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Graph Contrastive Learning for Anomaly Detection

https://github.com/alleinx/m3-impute

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M$^3$-Impute: Mask-guided Representation Learning for Missing Value Imputation

https://github.com/allminerlab/etr

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Erase then Rectify: A Training-Free Parameter Editing Approach for Cost-Effective Graph Unlearning

https://github.com/alpha-unito/benchmarking-normalization-layers-in-federated-learning-for-image-classification-tasks-on-non-iid

공식 코드

Experimenting with Normalization Layers in Federated Learning on non-IID scenarios

https://github.com/alphanumericslab/ecg-image-kit

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ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization

https://github.com/alps-lab/dpgan

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Differentially Private Releasing via Deep Generative Model (Technical Report)

https://github.com/amahdeej/sc2-edge-learning

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Knowledge Distillation For Wireless Edge Learning

https://github.com/amaiya/onprem

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OnPrem.LLM: A Privacy-Conscious Document Intelligence Toolkit

https://github.com/amayags/biox-cpath

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BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology

https://github.com/amayags/mustang

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MUSTANG: Multi-Stain Self-Attention Graph Multiple Instance Learning Pipeline for Histopathology Whole Slide Images

https://github.com/amazon-research/co-with-gnns-example

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Combinatorial Optimization with Physics-Inspired Graph Neural Networks

https://github.com/amazon-research/tgl

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TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs

https://github.com/amazon-science/convolution-matching

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Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training

https://github.com/amazon-science/dlwp-benchmark

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Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics

https://github.com/amazon-science/network-usable-info-framework

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NetInfoF Framework: Measuring and Exploiting Network Usable Information

https://github.com/amazon-science/page-link-path-based-gnn-explanation

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PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction

https://github.com/amberyzheng/bloomgml

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BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization

https://github.com/aminabyaneh/Federated_CL

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Federated Causal Discovery From Interventions

https://github.com/aminabyaneh/fed-cd

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Federated Causal Discovery From Interventions

https://github.com/aminabyaneh/fed-cdi

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Federated Causal Discovery From Interventions

https://github.com/amrhssn/teneNCE

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Contrastive Representation Learning for Dynamic Link Prediction in Temporal Networks

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