302 citations · 460 across the 7 of their papers we have counts for
6 papers · 1 filter
LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning
Kamala Varma, Yi Zhou, Nathalie Baracaldo +1
Federated learning has arisen as a mechanism to allow multiple participants to collaboratively train a model without sharing their data. In these settings, participants (workers) m…
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
Runhua Xu, Nathalie Baracaldo, Yi Zhou +3
Federated learning (FL) has been proposed to allow collaborative training of machine learning (ML) models among multiple parties where each party can keep its data private. In this…
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
Syed Zawad, Ahsan Ali, Pin-Yu Chen +5
Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on…
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas +21
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. How…
TiFL: A Tier-based Federated Learning System
Zheng Chai, Ahsan Ali, Syed Zawad +7
Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that ex…
A Hybrid Approach to Privacy-Preserving Federated Learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar +4
Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality durin…