190 citations · 255 across the 17 of their papers we have counts for
11 papers · 1 filter
DP-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization
Zhenxiao Zhang, Yuanxiong Guo, Yanmin Gong
Federated learning (FL) is a distributed machine learning approach that allows multiple clients to collaboratively train a model without sharing their raw data. To prevent sensitiv…
Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy
Zhenxiao Zhang, Yuanxiong Guo, Yuguang Fang +1
Federated Learning (FL) is a collaborative learning framework that enables edge devices to collaboratively learn a global model while keeping raw data locally. Although FL avoids l…
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 ADMM for Convex Distributed Learning: Improved Accuracy via Multi-Step Approximation
Zonghao Huang, Yanmin Gong
Alternating Direction Method of Multipliers (ADMM) is a popular algorithm for distributed learning, where a network of nodes collaboratively solve a regularized empirical risk mini…
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…
Differentially Private ADMM for Distributed Medical Machine Learning
Jiahao Ding, Xiaoqi Qin, Wenjun Xu +3
Due to massive amounts of data distributed across multiple locations, distributed machine learning has attracted a lot of research interests. Alternating Direction Method of Multip…
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…