302 citations · 775 across the 27 of their papers we have counts for
4 papers · 1 filter
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…