302 citations · 775 across the 27 of their papers we have counts for
3 papers · 1 filter
Federated XGBoost on Sample-Wise Non-IID Data
Katelinh Jones, Yuya Jeremy Ong, Yi Zhou +1
Federated Learning (FL) is a paradigm for jointly training machine learning algorithms in a decentralized manner which allows for parties to communicate with an aggregator to creat…
DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting
Runhua Xu, Nathalie Baracaldo, Yi Zhou +3
Federated learning has emerged as a privacy-preserving machine learning approach where multiple parties can train a single model without sharing their raw training data. Federated…
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis
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), a gener…