Publications

[Google Scholar] [Patents]
[Industrial R&D Experience] [Technical Reports] [Leetcode]
Potential Publication Venues:
[Machine Learning] ICML, NeurIPS, ICLR;
[ML/AI Applications] CVPR/ECCV/ICCV, ACL/EMNLP, AAAI/IJCAI;
[Distributed/Cloud/Mobile/Database Systems] NSDI, OSDI, SOSP, SC, VLDB, MobSys, MobiCom, Sensys, HotOS, EuroSys, ICDCS, etc.

Peer-Reviewed Conference Publications
(2019 – present; selected)

  • [13] Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection
    Tuo Zhang*, Chaoyang He*, Tianhao Ma, Lei Gao, Mark Ma, Salman Avestimehr
    (* means co-1st authors)
    accepted to ACM Embedded Networked Sensor Systems SenSys 2021 (AIChallengeIoT)
    [Proceeding] [Arxiv] [GitHub]
    TLDR: IoT x Federated Learning, from FedML.ai

  • [12] A Field Guide to Federated Optimization (with Google and CMU). 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
    TDLR: want to learn distributed/federated optimization? This is a good starting point!
  • [8] 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]
    TDLR: FedML Ecosystem (https://FedML.ai) is a family of open research libraries to facilitate federated learning research in diverse application domains. It includes FedML Core Framework, FedNLP (Natural Language Processing), FedCV (Computer Vision), FedGraphNN (Graph Neural Networks), FedIoT (Internet of Things), and FedMobile (Smartphones). With the fundamental support from FedML Core Framework, FedML Ecosystem enables three computing paradigms: on-device training for edge devices (cross-device FL), distributed computing (cross-silo FL), and single-machine simulation, and also promotes diverse algorithmic research with flexible and generic API design and comprehensive reference baseline implementations (federated optimizer, private/security algorithms, models, and datasets). Compared with TFF and LEAF, FedNLP and FedCV greatly enrich the diversity of data sets and learning tasks. FedNLP supports various popular task formulations in the NLP domain, such as text classification, sequence tagging, question answering, seq2seq generation, and language modeling. FedCV can help researchers evaluate the three most representative tasks: image classification, image segmentation, and object detection. Moreover, FedGraphNN is the first FL research platform for analyzing graph-structured data using Graph Neural Networks in a distributed computing manner, filling the gap between federated learning and the data mining field. FedGraphNN collects, preprocess, and partitions 36 datasets from 7 domains such as molecule ML (drug discovery, bioinformatics, etc.), Social networks, Recommendation Systems, and Knowledge Graphs. Going beyond traditional AI applications, FedIoT and FedMobile further extend FL to perform in wireless communication (e.g., 5G) and mobile computing (e.g., embedded IoT devices such as Raspberry PI, smartphones running on Android OS). All in all, FedML Ecosystem aims to provide a one-stop scientific research platform through FedML Ecosystem and finally realize trustworthy ML/AI, which is more secure, scalable, efficient, and ubiquitous.
  • [4] 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)
    TDLR: 105 pages, 485 references, 22 Googlers, and 36 academics at 24 institutions!
  • [3] 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]
    TDLR: Decentralized Federated Learning!

  • [2] 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
    [Arxiv]

  • [1] Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling
    Zi Long, Lianzhi Tan, Shengping Zhou, Chaoyang He, Xin Liu
    International Joint Conference on Neural Networks, IJCNN 2019
    [Arxiv]
    TDLR: natural language processing, security; work done when I was at Tencent, the company that developed WeChat…

Industrial R&D Experience

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)

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)