31 citations · 58 across the 8 of their papers we have counts for
8 papers
PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks
Shiwei Ding, Lan Zhang, Miao Pan +1
Collaborative inference has been a promising solution to enable resource-constrained edge devices to perform inference using state-of-the-art deep neural networks (DNNs). In collab…
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices
Peichun Li, Guoliang Cheng, Xumin Huang +4
In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjus…
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation
Liang Li, Chenpei Huang, Dian Shi +4
Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to pr…
Service Delay Minimization for Federated Learning over Mobile Devices
Rui Chen, Dian Shi, Xiaoqi Qin +3
Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay eff…
Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications
Tian Liu, Jiahao Ding, Ting Wang +2
As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL)is becoming popular in Internet of Things (IoT) des…
FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing
Peichun Li, Xumin Huang, Miao Pan +1
Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without uploading the local data. Gradient compression may be applied…
Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission
Rui Chen, Liang Li, Kaiping Xue +3
Federated learning (FL) is a popular collaborative distributed machine learning paradigm across mobile devices. However, practical FL over resource constrained mobile devices confr…
Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees
Di Wang, Jiahao Ding, Lijie Hu +3
(Gradient) Expectation Maximization (EM) is a widely used algorithm for estimating the maximum likelihood of mixture models or incomplete data problems. A major challenge facing th…