249 citations · 1.2k across the 42 of their papers we have counts for
25 papers · 1 filter
Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates
Guojun Xiong, Gang Yan, Shiqiang Wang +1
With the increasing demand for large-scale training of machine learning models, fully decentralized optimization methods have recently been advocated as alternatives to the popular…
Adaptive Federated Pruning in Hierarchical Wireless Networks
Xiaonan Liu, Shiqiang Wang, Yansha Deng +1
Federated Learning (FL) is a promising privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their privat…
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
Timothy Castiglia, Yi Zhou, Shiqiang Wang +3
We propose LESS-VFL, a communication-efficient feature selection method for distributed systems with vertically partitioned data. We consider a system of a server and several parti…
Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning
Hanjing Wang, Dhiraj Joshi, Shiqiang Wang +1
Predictions made by deep learning models are prone to data perturbations, adversarial attacks, and out-of-distribution inputs. To build a trusted AI system, it is therefore critica…
Federated Learning with Flexible Control
Shiqiang Wang, Jake Perazzone, Mingyue Ji +1
Federated learning (FL) enables distributed model training from local data collected by users. In distributed systems with constrained resources and potentially high dynamics, e.g.…
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data
Timothy Castiglia, Anirban Das, Shiqiang Wang +1
We propose Compressed Vertical Federated Learning (C-VFL) for communication-efficient training on vertically partitioned data. In C-VFL, a server and multiple parties collaborative…
A Unified Analysis of Federated Learning with Arbitrary Client Participation
Shiqiang Wang, Mingyue Ji
Federated learning (FL) faces challenges of intermittent client availability and computation/communication efficiency. As a result, only a small subset of clients can participate i…
Joint Coreset Construction and Quantization for Distributed Machine Learning
Hanlin Lu, Changchang Liu, Shiqiang Wang +4
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication…
KerGNNs: Interpretable Graph Neural Networks with Graph Kernels
Aosong Feng, Chenyu You, Shiqiang Wang +1
Graph kernels are historically the most widely-used technique for graph classification tasks. However, these methods suffer from limited performance because of the hand-crafted com…
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization
Jake Perazzone, Shiqiang Wang, Mingyue Ji +1
Federated learning (FL) is a useful tool in distributed machine learning that utilizes users' local datasets in a privacy-preserving manner. When deploying FL in a constrained wire…
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling
Bing Luo, Wenli Xiao, Shiqiang Wang +2
Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communicatio…
Cost-Effective Federated Learning in Mobile Edge Networks
Bing Luo, Xiang Li, Shiqiang Wang +2
Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server…
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning
Anirban Das, Timothy Castiglia, Shiqiang Wang +1
We consider federated learning in tiered communication networks. Our network model consists of a set of silos, each holding a vertical partition of the data. Each silo contains a h…
Communication-efficient k-Means for Edge-based Machine Learning
Hanlin Lu, Ting He, Shiqiang Wang +5
We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine lear…
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…
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…
Model Pruning Enables Efficient Federated Learning on Edge Devices
Yuang Jiang, Shiqiang Wang, Victor Valls +4
Federated learning (FL) allows model training from local data collected by edge/mobile devices while preserving data privacy, which has wide applicability to image and vision appli…
Robust Coreset Construction for Distributed Machine Learning
Hanlin Lu, Ming-Ju Li, Ting He +3
Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over dist…