Dimitri Lerévérend

Unified Privacy Guarantees for Decentralized Learning via Matrix Factorization

Decentralized Learning (DL) enables users to collaboratively train models without sharing raw data by iteratively averaging local updates with neighbors in a network graph. This setting is increasingly popular for its scalability and its ability to keep data local under user control. Strong privacy…