Chaoyang He

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Salman AvestimehrDean's Professor and Director of USC-Amazon Center on Trustworthy AI, USCDirección de correo verificada de usc.edu
Murali AnnavaramUSCDirección de correo verificada de usc.edu
Jinhyun SoAssistant Professor at DGISTDirección de correo verificada de dgist.ac.kr
Mi ZhangAssociate Professor, Director of AIoT and MLSys Lab, The Ohio State UniversityDirección de correo verificada de osu.edu
Mahdi SoltanolkotabiUniversity of Southern CaliforniaDirección de correo verificada de usc.edu
Sean (Xiang) RenViterbi Early Career Chair & Associate Professor, University of Southern CaliforniaDirección de correo verificada de usc.edu
Songze LiProfessor, Southeast UniversityDirección de correo verificada de seu.edu.cn
Emir CeyaniUniversity of Southern CaliforniaDirección de correo verificada de usc.edu
Tong ZhangUIUCDirección de correo verificada de tongzhang-ml.org
Bill Yuchen LinAffiliate Assistant Professor, University of WashingtonDirección de correo verificada de uw.edu
Praneeth VepakommaMassachusetts Institute of Technology, MBZUAIDirección de correo verificada de mit.edu
Qiang Yang (杨强）Chief AI Officer@WeBank, Prof. Emeritus @HKUST, IJCAI President 2017-19, AAAI-21 General ChairDirección de correo verificada de cse.ust.hk
Hongyi WangIncoming Assistant Professor, Rutgers UniversityDirección de correo verificada de andrew.cmu.edu
Shen LI (李珅)Facebook AI ResearchDirección de correo verificada de fb.com
Qian Yu (于乾)Princeton UniversityDirección de correo verificada de princeton.edu
Peter KairouzResearch Scientist, GoogleDirección de correo verificada de google.com
H. Brendan McMahanResearch Scientist, Google SeattleDirección de correo verificada de google.com
Tian LiUniversity of ChicagoDirección de correo verificada de uchicago.edu
Zheng XuGoogle ResearchDirección de correo verificada de google.com
Jianyu WangResearch Scientist at AppleDirección de correo verificada de apple.com

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Chaoyang He

Co-founder, TensorOpera AI and FedML; PhD in CS from University of Southern California

Dirección de correo verificada de usc.edu - Página principal

Machine Learning Generative AI Distributed Systems Blockchain AI Security and Privacy


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Foundations and Trends® in Machine Learning 14 (1–2), 1-210, 2021	6605	2021
FedML: A Research Library and Benchmark for Federated Machine Learning C He, S Li, J So, M Zhang, H Wang, X Wang, V Praneeth, S Abhishek, ... NeurIPS 2020 FL Workshop Best Paper Award, 2019	656*	2019
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge C He, M Annavaram, S Avestimehr NeurIPS 2020 (Advances in Neural InformationProcessing Systems 2020), 2020	485	2020
A field guide to federated optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ... arXiv preprint arXiv:2107.06917, 2021	381	2021
Federated learning for the internet of things: Applications, challenges, and opportunities T Zhang, L Gao, C He, M Zhang, B Krishnamachari, AS Avestimehr IEEE Internet of Things Magazine 5 (1), 24-29, 2022	293	2022
FedGraphNN: A Federated Learning Benchmark System for Graph Neural Networks C He, K Balasubramanian, E Ceyani, C Yang, H Xie, L Sun, L He, L Yang, ... ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML) and …, 2021	226*	2021
FairFed: Enabling Group Fairness in Federated Learning Y Ezzeldin, Y Shen, C He, E Ferrara, S Avestimehr AAAI 2023, 2023	189	2023
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning J So, C He, CS Yang, S Li, Y Qian, S Avestimehr MLSys 2022 - Fifth Conference on Machine Learning and Systems, 2022	188*	2022
FedNLP: A Research Platform for Federated Learning in Natural Language Processing BY Lin, C He, Z Zeng, H Wang, Y Huang, M Soltanolkotabi, X Ren, ... Accepted to NAACL 2022, 2022	176*	2022
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation C He, H Ye, L Shen, T Zhang CVPR 2020 (IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020), 2020	157	2020
Towards Non-IID and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search C He, M Annavaram, S Avestimehr CVPR 2020 Workshop on Neural Architecture Search and Beyond for …, 2020	146*	2020
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings J Ogier du Terrail, SS Ayed, E Cyffers, F Grimberg, C He, R Loeb, ... NeurIPS 2022, 2022	116	2022
Federated learning for internet of things T Zhang, C He, T Ma, L Gao, M Ma, S Avestimehr Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems …, 2021	100	2021
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data C He, E Ceyani, K Balasubramanian, M Annavaram, S Avestimehr AAAI 2022 - Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022	99*	2022
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge G Yuan, X Ma, W Niu, Z Li, Z Kong, N Liu, Y Gong, Z Zhan, C He, Q Jin, ... NeurIPS 2021 (Advances in Neural InformationProcessing Systems 2021), 2021	94	2021
Central Server Free Federated Learning over Single-sided Trust Social Networks C He, C Tan, H Tang, Q Shuang, J Liu NeurIPS 2020 (Advances in Neural InformationProcessing Systems 2020), 2020	90	2020
FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks C He, AD Shah, Z Tang, DFAN Sivashunmugam, K Bhogaraju, M Shimpi, ... AAAI 2022 - International Workshop on Trustable, Verifiable and Auditable …, 2021	78	2021
AutoCTS: Automated Correlated Time Series Forecasting X Wu, D Zhang, C Guo, C He, B Yang, CS Jensen VLDB 2022 - 48th International Conference on Very Large Data Bases, 2021	72	2021
Advances and open problems in federated learning. arXiv 2019 P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... arXiv preprint arXiv:1912.04977, 2019	67	2019
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models C He, S Li, M Soltanolkotabi, S Avestimehr ICML 2021 (International Conference on Machine Learning 2021), 4150-4159, 2021	63*	2021

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