Publication

Privacy-Preserving Machine Learning on Graphs

Related publications (60)

About
Privacy
Disclaimer

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Privacy-Preserving Machine Learning on Graphs

Graph Chatbot

Chat with Graph Search

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

The Privacy Power of Correlated Noise in Decentralized Learning

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

Data-Driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Equivariant Neural Architectures for Representing and Generating Graphs

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation

Privacy-preserving federated neural network training and inference

Deep Learning Generalization with Limited and Noisy Labels

Network Alignment With Holistic Embeddings

On the (In)security of Peer-to-Peer Decentralized Machine Learning