190 citations · 221 across the 8 of their papers we have counts for
7 papers · 1 filter
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy
Rui Hu, Yanmin Gong, Yuanxiong Guo
Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keeping their training data locally has received great attention recently and can pr…
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing
Zhidong Gao, Rui Hu, Yanmin Gong
Graph classification has practical applications in diverse fields. Recent studies show that graph-based machine learning models are especially vulnerable to adversarial perturbatio…
Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning
Rui Hu, Yanmin Gong
Federated Learning rests on the notion of training a global model distributedly on various devices. Under this setting, users' devices perform computations on their own data and th…
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization
Rui Hu, Yanmin Gong, Yuanxiong Guo
Federated learning (FL) enables distributed agents to collaboratively learn a centralized model without sharing their raw data with each other. However, data locality does not prov…
Differentially Private Federated Learning for Resource-Constrained Internet of Things
Rui Hu, Yuanxiong Guo, E. Paul. Ratazzi +1
With the proliferation of smart devices having built-in sensors, Internet connectivity, and programmable computation capability in the era of Internet of things (IoT), tremendous d…
Concentrated Differentially Private and Utility Preserving Federated Learning
Rui Hu, Yuanxiong Guo, Yanmin Gong
Federated learning is a machine learning setting where a set of edge devices collaboratively train a model under the orchestration of a central server without sharing their local d…
DP-ADMM: ADMM-based Distributed Learning with Differential Privacy
Zonghao Huang, Rui Hu, Yuanxiong Guo +2
Alternating Direction Method of Multipliers (ADMM) is a widely used tool for machine learning in distributed settings, where a machine learning model is trained over distributed da…