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Privacy Preserving

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코드

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

https://github.com/dmlc/dgl/tree/master/examples/pytorch/GNN-FiLM

GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation

https://github.com/dmlc/dgl/tree/master/examples/pytorch/P-GNN

Position-aware Graph Neural Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/arma

Graph Neural Networks with convolutional ARMA filters

https://github.com/dmlc/dgl/tree/master/examples/pytorch/caregnn

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

https://github.com/dmlc/dgl/tree/master/examples/pytorch/dagnn

Towards Deeper Graph Neural Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/diffpool

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/dmlc/dgl/tree/master/examples/pytorch/gnn_explainer

GNNExplainer: Generating Explanations for Graph Neural Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/hgp_sl

Hierarchical Graph Pooling with Structure Learning

https://github.com/dmlc/dgl/tree/master/examples/pytorch/line_graph

Supervised Community Detection with Line Graph Neural Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/seal

Link Prediction Based on Graph Neural Networks

https://github.com/dodo47/SpikE

Learning through structure: towards deep neuromorphic knowledge graph embeddings

https://github.com/donghwan-rho/Encryption-friendly_LLM_Architecture

Encryption-Friendly LLM Architecture

https://github.com/dssg/aequitas

Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation

https://github.com/dsulem/dynnet

Graph similarity learning for change-point detection in dynamic networks

https://github.com/dual-grp/fedpca_anomalydetection

Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks

https://github.com/dual-grp/pFedMe

Personalized Federated Learning with Moreau Envelopes

https://github.com/edapinenut/cbgbench

Generating 3D Molecules for Target Protein Binding

https://github.com/ekaksher/FedCTR

FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior Data

https://github.com/epfl-imos/htgnn

Graph Neural Networks for Virtual Sensing in Complex Systems: Addressing Heterogeneous Temporal Dynamics

https://github.com/epicosy/devign

Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

https://github.com/eth-sri/bayes-framework-leakage

Privacy-preserving Collaborative Learning with Automatic Transformation Search

https://github.com/eth-sri/llm-anonymization

A Synthetic Dataset for Personal Attribute Inference

https://github.com/evtimovi/foggysight

FoggySight: A Scheme for Facial Lookup Privacy

https://github.com/facebookresearch/SEAL_OGB

Link Prediction Based on Graph Neural Networks

https://github.com/facebookresearch/ppuda

Graph HyperNetworks for Neural Architecture Search

https://github.com/facebookresearch/taser-tgnn

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

https://github.com/fangwei123456/spikingjelly

HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors

https://github.com/farakiko/xai4hep

Explaining machine-learned particle-flow reconstruction

https://github.com/fedlearnAI/fedlearnalgo

Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform

https://github.com/fedlearnJDT/fedlearn-algo

Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform

https://github.com/felisat/clustered-federated-learning

Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints

https://github.com/felisat/federated-learning

Robust and Communication-Efficient Federated Learning from Non-IID Data

https://github.com/fidler-lab/polyrnn-pp

Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++

https://github.com/fidler-lab/polyrnn-pp-pytorch

Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++

https://github.com/flexible-fl/flex-trees

A Hybrid Approach to Privacy-Preserving Federated Learning

https://github.com/flint-xf-fan/Byzantine-Federeated-RL

FedRLHF: A Convergence-Guaranteed Federated Framework for Privacy-Preserving and Personalized RLHF

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.