Ang Li


I am currently a Research Associate in Qualcomm AI Research. Before joining Qualcomm, I obtained Ph.D. from Duke University under the supervision of Professor Yiran Chen and Hai “Helen” Li in Duke CEI Lab I will join the Department of Electrical and Computer Engineering at University of Maryland, College Park as a tenure-track assistant professor. My research interests lie in the intersection of machine learning and edge computing, with a focus on building large-scale networked and trustworthy intelligent systems to solve practical problems in a collaborative, scalable, secure, and ubiquitous manner.

Openings: I’m looking for highly motivated Ph.D. and master students, and research interns to join my group. If you are interested, please send an email to with you CV and transcripts attached.


04/27/2023 Our research paper titled “Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction” has been accepted for presentation at ICML 2023.
04/25/2023 I am beyond thrilled to share that I have been selected as one of the two recipients for the ECE Department Outstanding Ph.D. Dissertation Award for the academic year 22-23 at Duke University!
03/15/2023 Our paper “AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving” has been accepted by MobiCom 2023!
10/10/2022 Our paper “FedSEA: A Semi-Asynchronous Federated Learning Framework for Extremely Heterogeneous Devices” has been accepted by SenSys 2022!
10/01/2022 Our paper “GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs” has been accepted by ICDM 2022!

Selected Publications

  1. SEC
    LotteryFL: empower edge intelligence with personalized and communication-efficient federated learning
    Ang Li, Jingwei Sun, Binghui Wang, and 4 more authors
    In 2021 IEEE/ACM Symposium on Edge Computing (SEC), 2021
  2. KDD
    TIPRDC: task-independent privacy-respecting data crowdsourcing framework for deep learning with anonymized intermediate representations
    Ang Li, Yixiao Duan, Huanrui Yang, and 2 more authors
    In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020
  3. MobiCom
    Hermes: an efficient federated learning framework for heterogeneous mobile clients
    Ang Li, Jingwei Sun, Pengcheng Li, and 3 more authors
    In Proceedings of the 27th Annual International Conference on Mobile Computing and Networking, 2021
  4. SenSys
    Fedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking
    Ang Li, Jingwei Sun, Xiao Zeng, and 3 more authors
    In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, 2021
  5. MobiCom
    AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving
    Tianyue Zheng, Ang Li, Zhe Chen, and 2 more authors