2 citations · 3 across the 6 of their papers we have counts for
6 papers
FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning
Phung Lai, Xiaopeng Jiang, Hai Phan +5
Federated Learning (FL) allows collaborative training among multiple devices without data sharing, thus enabling privacy-sensitive applications on mobile or Internet of Things (IoT…
Concept Matching: Clustering-based Federated Continual Learning
Xiaopeng Jiang, Cristian Borcea
Federated Continual Learning (FCL) has emerged as a promising paradigm that combines Federated Learning (FL) and Continual Learning (CL). To achieve good model accuracy, FCL needs…
Zone-based Federated Learning for Mobile Sensing Data
Xiaopeng Jiang, Thinh On, NhatHai Phan +5
Mobile apps, such as mHealth and wellness applications, can benefit from deep learning (DL) models trained with mobile sensing data collected by smart phones or wearable devices. H…
Complement Sparsification: Low-Overhead Model Pruning for Federated Learning
Xiaopeng Jiang, Cristian Borcea
Federated Learning (FL) is a privacy-preserving distributed deep learning paradigm that involves substantial communication and computation effort, which is a problem for resource-c…
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps
Xiaopeng Jiang, Han Hu, Vijaya Datta Mayyuri +6
This article presents the design, implementation, and evaluation of FLSys, a mobile-cloud federated learning (FL) system, which can be a key component for an open ecosystem of FL m…
FGLP: A Federated Fine-Grained Location Prediction System for Mobile Users
Xiaopeng Jiang, Shuai Zhao, Guy Jacobson +6
Fine-grained location prediction on smart phones can be used to improve app/system performance. Application scenarios include video quality adaptation as a function of the 5G netwo…