@article{chaoyanghe2020fedml,
  Author = {He, Chaoyang and Li, Songze and So, Jinhyun and Zhang, Mi and Wang, Hongyi and Wang, Xiaoyang and Vepakomma, Praneeth and Singh, Abhishek and Qiu, Hang and Shen, Li and Zhao, Peilin and Kang, Yan and Liu, Yang and Raskar, Ramesh and Yang, Qiang and Annavaram, Murali and Avestimehr, Salman},
  Journal = {arXiv preprint arXiv:2007.13518},
  Title = {FedML: A Research Library and Benchmark for Federated Machine Learning},
  Year = {2020}
}

FedML: A Research Library and Benchmark for Federated Machine Learning

@inproceedings{FedGKT2020,
  title={Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge},
  author={He, Chaoyang and Annavaram, Murali and Avestimehr, Salman},
  booktitle = {Advances in Neural Information Processing Systems 34},
  year={2020}
}

Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge

        @inproceedings{MiLeNAS2020,
          title={MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation},
          author={He, Chaoyang and Ye, Haishan and Shen, Li and Zhang, Tong},
          booktitle={Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
          year={2020},
        }

MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation

@article{he2020fednas,
  title={FedNAS: Federated Deep Learning via Neural Architecture Search},
  author={He, Chaoyang and Annavaram, Murali and Avestimehr, Salman},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshop on Beyond Neural Architecture Search},
  year={2020}
}

FedNAS: Federated Deep Learning via Neural Architecture Search

        @article{he2019central,
          title={Central server free federated learning over single-sided trust social networks},
          author={He, Chaoyang and Tan, Conghui and Tang, Hanlin and Qiu, Shuang and Liu, Ji},
          journal={arXiv preprint arXiv:1910.04956},
          year={2019}
        }

Central server free federated learning over single-sided trust social networks

        @article{he2019bipartite,
          title={Bipartite Graph Neural Networks for Efficient Node Representation Learning},
          author={He, Chaoyang and Xie, Tian and Rong, Yu and Huang, Wenbing and Li, Yanfang and Huang, Junzhou and Ren, Xiang and Shahabi, Cyrus},
          journal={arXiv preprint arXiv:1906.11994},
          year={2019}
        }

Bipartite Graph Neural Networks for Efficient Node Representation Learning

        @article{kairouz2019advances,
          title={Advances and open problems in federated learning},
          author={Kairouz, Peter and McMahan, H Brendan and Avent, Brendan and Bellet, Aur{\'e}lien and Bennis, Mehdi and Bhagoji, Arjun Nitin and Bonawitz, Keith and Charles, Zachary and Cormode, Graham and Cummings, Rachel and others},
          journal={arXiv preprint arXiv:1912.04977},
          year={2019}
        }

Advances and open problems in federated learning

        @inproceedings{long2019collecting,
          title={Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling},
          author={Long, Zi and Tan, Lianzhi and Zhou, Shengping and He, Chaoyang and Liu, Xin},
          booktitle={2019 International Joint Conference on Neural Networks (IJCNN)},
          pages={1--8},
          year={2019},
          organization={IEEE}
        }

Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling

        @inproceedings{anastasiou2019admsv2,
          title={ADMSv2: A Modern Architecture for Transportation Data Management and Analysis},
          author={Anastasiou, Chrysovalantis and Lin, Jianfa and He, Chaoyang and Chiang, Yao-Yi and Shahabi, Cyrus},
          booktitle={Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities},
          pages={25--28},
          year={2019}
        }

ADMSv2: A Modern Architecture for Transportation Data Management and Analysis