302 citations · 695 across the 16 of their papers we have counts for
4 papers · 1 filter
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning
Sixing Yu, Phuong Nguyen, Waqwoya Abebe +3
Federated learning~(FL) facilitates the training and deploying AI models on edge devices. Preserving user data privacy in FL introduces several challenges, including expensive comm…
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…