Publications

[Semantics Scholar]
[Production] [Patents] [Technical Reports] [Leetcode]

Venue: ICML, NeurIPS, ICLR, CVPR, MLSys, etc.

Peer-Reviewed Conference Publications
(2019 – present, selected, 1st-author)

• PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers
  Chaoyang He, Shen Li (Facebook AI Research), Mahdi Soltanolkotabi, Salman Avestimehr
  Accepted to ICML 2021 (International Conference on Machine Learning 2021)
  [Arxiv] [Proceeding] [Homepage] [Slides] [Animation] [Open Source Code] [DistML.ai]

• FedML: A Research Library and Benchmark for Federated Machine Learning
  Chaoyang He, Songze Li (HKUST, Stanford), Jinhyun So (USC), Mi Zhang (USC), Hongyi Wang (U Wisconsin Madison), Xiaoyang Wang (UIUC), Praneeth Vepakomma (MIT), Abhishek Singh (MIT), Hang Qiu (Stanford), Li Shen (Tencent), Peilin Zhao (Tencent), Yan Kang (WeBank), Yang Liu (WeBank), Ramesh Raskar (MIT), Qiang Yang (HKUST, WeBank), Murali Annavaram, Salman Avestimehr
  The short version of FedML white paper won Best Paper Award at SpicyFL@NeurIPS 2020
  [BibTex] [Homepage] [Arxiv] [Slack] [Documentation] [Video] [Slides] [Best Paper Award] [Code]
  Highlights: We are building an open-source project for federated learning! https://fedml.ai

• Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
  Chaoyang He, Murali Annavaram, Salman Avestimehr
  Accepted to NeurIPS 2020 (Conference on Neural Information Processing Systems 2020)
  [BibTex] [Arxiv] [NeurIPS Official Site Video Presentation] [Gather Town] [Project Page] [Poster] [Slides] [Code]

• MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation
  Chaoyang He^*, Haishan Ye^*, Li Shen, Tong Zhang (HKUST, Tencent AI Lab)
  Accepted to CVPR 2020 (IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020)
  [BibTex] [arxiv] [video] [Code]

Peer-Reviewed Workshop Publications
(2019 – present)

• FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
  Chaoyang He^*, Keshav Balasubramanian^*, Emir Ceyani^*, Yu Rong (Tencent), Peilin Zhao (Tencent), Junzhou Huang (Tencent), Murali Annavaram, Salman Avestimehr
  (* equal contribution)
  A short version has been accepted to DPML@ICLR 2021 and GNNSys@MLSys 2021
  [ICLR 2021 Workshop Video Presentation] [MLSys 2021 Workshop Camera Ready Version] [MLSys 2021 Poster] [Code]

• Central Server Free Federated Learning over Single-sided Trust Social Networks
  Chaoyang He, Conghui Tan (WeBank), Hanlin Tang, Shuang Qiu, Ji Liu (Kwai AI Lab, Professor at University of Rochester)
  Preprint, 2020
  A short version has been accepted to SpicyFL@NeurIPS 2020 (Lightning Talk)
  [BibTex] [Arxiv] [Lightning Talk] [Poster] [Code]

• Towards non-I.I.D. and invisible data with FedNAS: Federated Deep Learning via Neural Architecture Search
  Chaoyang He, Murali Annavaram, Salman Avestimehr
  Accepted to CVPR 2020 Workshop on Neural Architecture Search and Beyond for Representation Learning, 2020
  [BibTex] [Arxiv] [video] [Code]

Vision/Field Guide/Review Papers
(2019 – present, selected)

• (preparing) Private AI, with Intel. 2021
  
• A Field Guide to Federated Optimization (with Googlers). 2021
  Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecny, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtarik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu
  
• Advances and Open Problems in Federated Learning (with Googlers). 2019
  Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael GL d’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adria Gascón, Badih Ghazi, Phillip B Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrede Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X Yu, Han Yu, Sen Zhao
  [BibTex] [Arxiv] [Jeff Dean’s Tweet] [Publication at Foundations and Trends in Machine Learning]
  Published at FnTML 2020 (Foundations and Trends in Machine Learning, the chief editor is Michael Jordan)
  Highlight: 105 pages, 485 references, 22 Googlers, and 36 academics at 24 institutions!

Preprint

• Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection
  Tuo Zhang*, Chaoyang He*, Tianhao Ma, Mark Ma, Salman Avestimehr
  (* equal contribution)
  preprint, 2021
  
  
• SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
  Chaoyang He*, Emir Ceyani*, Keshav Balasubramanian*, Murali Annavaram, Salman Avestimehr
  (* equal contribution)
  preprint, 2021
  
  
• SSFL: Self-supervised and Personalized Federated Learning
  Chaoyang He, Zhengyu Yang, Erum Mushtaq, Sunwoo Lee, Mahdi Soltanolkotabi, Salman Avestimehr
  preprint, 2021
  
  
• LightSecAgg: Rethinking Secure Aggregation in Federated Learning
  Chien-Sheng Yang, Jinhyun So, Chaoyang He, Songze Li, Qian Yu, Salman Avestimehr
  preprint, 2021
  
  
• Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits
  Sunwoo Lee, Anit Kumar Sahu, Chaoyang He, Salman Avestimehr
  preprint, 2021
  
  

• Lightweight Image Super-Resolution with Hierarchical and Differentiable Neural Architecture Search
  Han Huang, Li Shen (JD Explore Academy), Chaoyang He, Weisheng Dong, Hao-Zhi Huang, Guangming Shi
  Preprint, 2021
  keywords: AutoML, Neural Architecture Search, Model Compression, Computer Vision
  (collaborated with my former colleague)
  
• MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
  Geng Yuan, Xiaolong Ma, Wei Niu, Ning Liu, Zhengang Li, Yifan Gong, Zheng Zhan, Chaoyang He, Bin Ren, Sijia Liu, Xue Lin, Yanzhi Wang (Northeastern University, USC Alumni)
  Preprint, 2021
  keywords: Efficient Edge Training
  

• FedNLP: A Research Platform for Federated Learning in Natural Language Processing
  Bill Yuchen Lin*, Chaoyang He*, Zihang Zeng, Hulin Wang, Yufen Huang, Mahdi Soltanolkotabi, Xiang Ren, Salman Avestimehr
  (* equal contribution)
  preprint, 2021
  [Code] [Arxiv]
  

• FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks
  Chaoyang He, Alay Dilipbhai Shah, Zhenheng Tang, Di Fan, Adarshan Naiynar Sivashunmugam, Keerti Bhogaraju, Mita Shimpi, Li Shen, Xiaowen Chu, Mahdi Soltanolkotabi, Salman Avestimehr
  Preprint, 2021

• Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs
  Chaoyang He^*, Tian Xie^*, Yu Rong, Wenbing Huang, Junzhou Huang, Xiang Ren, Cyrus Shahabi
  Preprint, 2020
  [BibTex] [Arxiv] [Code]

Production

I was a full-stack software engineer and team manager. I have lots of experience in successful Internet product publication. My experience spans nearly all Internet-related software systems development, including cloud computing, distributed systems, machine learning systems, applied machine learning, operating system, and mobile applications. Business domain: cloud computing, automotive intelligent system, autonomous driving, smartphone, Maps/LBS, games, etc

• Tencent Cloud. 2017-2018
  Worked on business-oriented cloud computing solution; led a team to develop the 1st generation cloud computing solution for the automotive industry.

• Tencent Venus Distributed Machine Learning System. 2016-2017
  Worked with Shengping Zhou (now CTO@AlphaCloud), developing the large-scale distributed machine learning system for MIG, Tencent. This platform supports various machine learning algorithms and models, including, LR, SVM, GBDT, DNN, etc. One more thing: I have to highlight that one module of Tencent Venus contains the first practice of federated learning system in China, even in the world, earlier than Google FL. At Tencent, we supported vertical federated learning infrastructure for WeBank to model Tencent social network features into their financial models.

• Tencent Location Data Computing Platform. 2016-2017
  worked on streaming data mining systems and algorithms; this platform contains billion-level daily users; I learned the entire system architecture and related real-time data analytics algorithms.

• Speech Recognition and Natural Language Processing for Tencent Automotive Operating System. 2015-2016
  This project inspired me to explore ML models, algorithms, and systems; deep learning is very fancy that year; we developed a deep learning model compression engine for our product (at that time, TensorFlow Lite is not released yet)

• Tencent Games: a Pokemon Go-like Mobile Game Engine 2016
  worked on Unity3D and C++ -based game engine; this is a cross-group collaboration, very fun; I learned a lot of engineering and production culture at Tencent Game.

• Tencent AI in Car (also called Tencent Automotive Service). 2014-2016
  I was leading an operating system team; worked on the embedded operating system (Android/QNX) and related back-end cloud services, including more than 10 applications and system core service, such as WeChat MyCar, Tencent Maps, Tencent Music, Tencent Video, etc. Please watch the video below to have a taste how Chinese Internet companies advertise their products:-)
  

• As a Team Leader, I Developed the 1st generation of Smartphone Navigation SDK, Application, and Service in China (Baidu Navigation SDK). 2012-2013
  Baidu Navigation SDK, Application, and Service is a cross-platform (C++/Java/Objective-C) engine that requires computer graphics (OpenGL ES), routing algorithms (A*, CH), offline and online data compiler and networking service (Hadoop; C++/PHP backend), complex navigation UI message state control and dispatch, cross-platform, layered, and modularized complex system design. I am familiar with all of these core modules.

• As a Software Engineer, I Contributed to the First Generation of Huawei Smartphone. 2011-2012
  The picture below shows Huawei Ascend P6 (Emotion UI)

Patents

• LightSecAgg: Rethinking Secure Aggregation in Federated Learning (under review)
  US patent
• Method for recognizing semantics, device storage medium and electronic device
  Patent number: CN109933774A
• A kind of voice recognition processing method and device
  Patent number: CN107293294A
• The data-pushing of vehicle cloud platform, treating method and apparatus
  Patent number: CN108833489A
• Method and apparatus for playing instant message sound on vehicle-mounted terminal
  Patent number: CN105530171A
• Resource obtaining method, terminal and vehicle terminal
  Patent number: CN105610978A
• Information interaction method and vehicle terminal
  Patent number: CN105791395A
• A kind of application development method, device, equipment and development frame
  Patent number: CN107179916A
• The rendering method and device of map, storage medium, electronic device
  Patent number: CN109544658A
• Selective recording method after realizing break-in of dispatching console in digital cluster system
  Patent number: CN101969444A

Technical Report

My industrial experience spans cloud computing, distributed system, applied machine learning, data computing platform, mobile computing (Android/iOS/IoT devices), and their applications in AI, Speech Recognition, Games, Maps, and Navigation.

• 2018 – Data-Driven Cloud Computing Platform and Machine Learning System for the Internet of Vehicles
• 2016 – The Large-Scale Distributed System and Real-Time Location Data Mining
• 2017 – TAI: An Intelligent Operating System and Open Platform
• 2016 – Speech Recognition System for Connected and Autonomous Driving Car
• 2016 – WeGameMap: Real World Map Rendering Engine for Location-Based Mobile Game
• 2015 – WeLink: a High-Performance Vehicle-Mobile Networking Library
• 2014 – TMAP: A System Framework for Mobile Maps and Navigation
• 2016 – Efficient Spatial Anti-Aliasing Rendering for Line Joins on Vector Maps
• 2014 – WeCross: a Mobile and Vehicle C/C++ Cross-Platform Library
• 2017 – A High Precision Private Car Trajectory Dataset and an Open Source Location Data Computing Platform
• 2017 – A Open Source High Reliable Location SDK for the Automotive Industry
• 2013 – The First Generation Mobile Navigation SDK Design in China
• 2012 – Map State Switching under Multi-trigger Condition Based on Finite State Machine Design Pattern
• 2012 – A General Downloader Engine for iOS/Android Platform
• 2012 – The Best Practice for Java Native Interface (JNI) Development on Android Operating Platform
• 2014 – Taxi-Calling Platform and the Open Source Code
• 2015 – A UX design of Bluetooth Steering Wheel Wireless Controller and the communication system design
• 2016 – Sliding Window Algorithm Template to Solve All the Leetcode Substring Search Problem.

Leetcode

An Algorithmic Source Code Template for Solving Many Substring Search Problems, 2016
(1.8K upvote, 2000+ stars, 167.8K views)