7 citations · 12 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 5 cited
IP-FL: Incentivized and Personalized Federated Learning
Ahmad Faraz Khan, Xinran Wang, Qi Le +7
Existing incentive solutions for traditional Federated Learning (FL) focus on individual contributions to a single global objective, neglecting the nuances of clustered personaliza…
cs.LG2022★ 7 cited
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
Ahmad Faraz Khan, Yuze Li, Xinran Wang +5
Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It's particularly popular for Edge and IoT ap…
cs.DC2020
Prediction of High-Performance Computing Input/Output Variability and Its Application to Optimization for System Configurations
Li Xu, Thomas Lux, Tyler Chang +6
Performance variability is an important measure for a reliable high performance computing (HPC) system. Performance variability is affected by complicated interactions between nume…