Search Results for author:
Found 6 papers, 5 papers with code
Privacy Attacks in Decentralized Learning
Decentralized Gradient Descent (D-GD) allows a set of users to perform collaborative learning without sharing their data by iteratively averaging local model updates with their neighbors in a network graph.
Differentially Private Decentralized Learning with Random Walks
The popularity of federated learning comes from the possibility of better scalability and the ability for participants to keep control of their data, improving data security and sovereignty.
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning
We study differentially private (DP) machine learning algorithms as instances of noisy fixed-point iterations, in order to derive privacy and utility results from this well-studied framework.
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
In this work, we introduce pairwise network differential privacy, a relaxation of LDP that captures the fact that the privacy leakage from a node $u$ to a node $v$ may depend on their relative position in the graph.
Privacy Amplification by Decentralization
In this work, we introduce a novel relaxation of local differential privacy (LDP) that naturally arises in fully decentralized algorithms, i. e., when participants exchange information by communicating along the edges of a network graph without central coordinator.
