Chaoyang He
Chaoyang He
Co-founder & CTO at FedML Inc; PhD in CS from University of Southern California
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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
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
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
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
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
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
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
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
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
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
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
Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs
C He, T Xie, Y Rong, W Huang, J Huang, X Ren, C Shahabi
TNNLS Journal - IEEE Transactions on Neural Networks and Learning Systems, 2020
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
FairFed: Enabling Group Fairness in Federated Learning
Y Ezzeldin, Y Shen, C He, E Ferrara, S Avestimehr
AAAI 2023, 2023
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
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
Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling
Z Long, L Tan, S Zhou, C He, X Liu
IJCNN 2019 (International Joint Conference on Neural Networks 2019), 2019
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
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
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
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