7 papers
Self-Creating Random Walks for Decentralized Learning under Pac-Man Attacks
Xingran Chen, Parimal Parag, Rohit Bhagat +1
Random walk (RW)-based algorithms have long been popular in distributed systems due to low overheads and scalability, with recent growing applications in decentralized learning. Ho…
Random Walk Learning and the Pac-Man Attack
Xingran Chen, Parimal Parag, Rohit Bhagat +2
Random walk (RW)-based algorithms have long been popular in distributed systems due to low overheads and scalability, with recent growing applications in decentralized learning. Ho…
Perfect Privacy and Strong Stationary Times for Markovian Sources
Fangwei Ye, Zonghong Liu, Parimal Parag +1
We consider the problem of sharing correlated data under a perfect information-theoretic privacy constraint. We focus on redaction (erasure) mechanisms, in which data are either wi…
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 MaÃny, 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…
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…