77 citations · 249 across the 25 of their papers we have counts for
32 papers
Decentralized Learning via Random Walk with Jumps
Zonghong Liu, Matthew Dwyer, Salim El Rouayheb
We study decentralized learning over networks where data are distributed across nodes without a central coordinator. Random walk learning is a token-based approach in which a singl…
Between Close Enough to Reveal and Far Enough to Protect: a New Privacy Region for Correlated Data
Luis Maßny, Rawad Bitar, Fangwei Ye +1
When users make personal privacy choices, correlation between their data can cause inadvertent leakage about users who do not want to share their data by other users sharing their…
Self-Regulating Random Walks for Resilient Decentralized Learning on Graphs
Maximilian Egger, Rawad Bitar, Ghadir Ayache +2
Consider the setting of multiple random walks (RWs) on a graph executing a certain computational task. For instance, in decentralized learning via RWs, a model is updated at each i…
Secure Distributed Matrix Multiplication with Precomputation
Ryann Cartor, Rafael G. L. D'Oliveira, Salim El Rouayheb +3
We consider the problem of secure distributed matrix multiplication in which a user wishes to compute the product of two matrices with the assistance of honest but curious servers.…
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks
Natalie Lang, Nir Shlezinger, Rafael G. L. D'Oliveira +1
Federated learning (FL) is an emerging paradigm that allows a central server to train machine learning models using remote users' data. Despite its growing popularity, FL faces cha…
Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Data
Ghadir Ayache, Venkat Dassari, Salim El Rouayheb
We consider the problem of a Parameter Server (PS) that wishes to learn a model that fits data distributed on the nodes of a graph. We focus on Federated Learning (FL) as a canonic…