302 citations · 497 across the 8 of their papers we have counts for
9 papers
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…
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning
Yuya Jeremy Ong, Yi Zhou, Nathalie Baracaldo +1
Federated Learning (FL) is an approach to collaboratively train a model across multiple parties without sharing data between parties or an aggregator. It is used both in the consum…
Mitigating Bias in Federated Learning
Annie Abay, Yi Zhou, Nathalie Baracaldo +3
As methods to create discrimination-aware models develop, they focus on centralized ML, leaving federated learning (FL) unexplored. FL is a rising approach for collaborative ML, in…
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…