Attention:The NSF Public Access Repository (PAR) system and access will be unavailable from 11:00 PM ET on Thursday, August 13 until 12:00 AM ET on Friday, August 14 due to maintenance. We apologize for the inconvenience.


Title: DP-ADMM: ADMM-Based Distributed Learning With Differential Privacy
Award ID(s):
1850523
PAR ID:
10183073
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
IEEE Transactions on Information Forensics and Security
Volume:
15
ISSN:
1556-6013
Page Range / eLocation ID:
1002 to 1012
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
  2. We provide new connections between two distinct federated learning approaches based on (i) ADMM and (ii) Variational Bayes (VB), and propose new variants by combining their complementary strengths. Specifically, we show that the dual variables in ADMM naturally emerge through the "site" parameters used in VB with isotropic Gaussian covariances. Using this, we derive two versions of ADMM from VB that use flexible covariances and functional regularisation, respectively. Through numerical experiments, we validate the improvements obtained in performance. The work shows connection between two fields that are believed to be fundamentally different and combines them to improve federated learning. 
    more » « less