302 citations · 775 across the 27 of their papers we have counts for
4 papers · 1 filter
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning
Yi Zhou, Parikshit Ram, Theodoros Salonidis +3
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss suRface Aggregation (FLoRA), the fir…
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing
Cheng Chen, Bhavya Kailkhura, Ryan Goldhahn +1
Federated learning is an emerging data-private distributed learning framework, which, however, is vulnerable to adversarial attacks. Although several heuristic defenses are propose…
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…