2 citations · 2 across the 5 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2025
FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training
Haoyuan Li, Mathias Funk, Jindong Wang +1
Federated Active Learning (FAL) has emerged as a promising framework to leverage large quantities of unlabeled data across distributed clients while preserving data privacy. Howeve…
cs.LG2024
Collaboratively Learning Federated Models from Noisy Decentralized Data
Haoyuan Li, Mathias Funk, Nezihe Merve Gürel +1
Federated learning (FL) has emerged as a prominent method for collaboratively training machine learning models using local data from edge devices, all while keeping data decentrali…
cs.LG2024★ 2 cited
Empowering Data Mesh with Federated Learning
Haoyuan Li, Salman Toor
The evolution of data architecture has seen the rise of data lakes, aiming to solve the bottlenecks of data management and promote intelligent decision-making. However, this centra…