249 citations · 1.2k across the 42 of their papers we have counts for
9 papers · 1 filter
Cost-Effective Federated Learning Design
Bing Luo, Xiang Li, Shiqiang Wang +2
Federated learning (FL) is a distributed learning paradigm that enables a large number of devices to collaboratively learn a model without sharing their raw data. Despite its pract…
Robustness and Diversity Seeking Data-Free Knowledge Distillation
Pengchao Han, Jihong Park, Shiqiang Wang +1
Knowledge distillation (KD) has enabled remarkable progress in model compression and knowledge transfer. However, KD requires a large volume of original data or their representatio…
Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD
Jiayi Wang, Shiqiang Wang, Rong-Rong Chen +1
Hierarchical SGD (H-SGD) has emerged as a new distributed SGD algorithm for multi-level communication networks. In H-SGD, before each global aggregation, workers send their updated…
Sharing Models or Coresets: A Study based on Membership Inference Attack
Hanlin Lu, Changchang Liu, Ting He +2
Distributed machine learning generally aims at training a global model based on distributed data without collecting all the data to a centralized location, where two different appr…
Online Learning of Facility Locations
Stephen Pasteris, Ting He, Fabio Vitale +2
In this paper, we provide a rigorous theoretical investigation of an online learning version of the Facility Location problem which is motivated by emerging problems in real-world…
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art
Ahmed Imteaj, Urmish Thakker, Shiqiang Wang +2
Nowadays, devices are equipped with advanced sensors with higher processing/computing capabilities. Further, widespread Internet availability enables communication among sensing de…
Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice
Shufan Wang, Jian Li, Shiqiang Wang
We study the problem of augmenting online algorithms with machine learned (ML) advice. In particular, we consider the \emph{multi-shop ski rental} (MSSR) problem, which is a genera…
Overcoming Noisy and Irrelevant Data in Federated Learning
Tiffany Tuor, Shiqiang Wang, Bong Jun Ko +2
Many image and vision applications require a large amount of data for model training. Collecting all such data at a central location can be challenging due to data privacy and comm…
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
Pengchao Han, Shiqiang Wang, Kin K. Leung
Federated learning (FL) is an emerging technique for training machine learning models using geographically dispersed data collected by local entities. It includes local computation…