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Jiaqi Shao

Federated Learning, Distributed Edge AI, Privacy-Preserving Machine Learning.

Education

Hong Kong University of Science and Technology (2023 Fall -)
Doctor of Philosophy (PhD) in Electronic and Computer Engineering

My supervior: Prof. Wei Zhang (HKUST) (Mentor: Prof. Bing Luo (DKU))

The Chinese University of Hong Kong, Shenzhen (2019 — 2023)
Bachelor of Engineering in Electrical and Computer Engineer, Stream: Computer Engineering

Publications

  1. Shao, J., Lin, T., Xiaojin Zhang Qiang Yang, and Luo, B., "Beyond Right to be Forgotten: Managing Heterogeneity Side Effects Through Strategic Incentives", ACM MobiHoc 2025, 2025. (Accepted) 🎉
  2. Lu, S., Shao, J., Luo, B., and Lin, T., "Morphagent: Empowering agents through self-evolving profiles and decentralized collaboration", ICML-MAS, 2025.
  3. Shao, J.,Yuan, T., Lin, T., Cao, X., and Luo, B., Cognitive Insights and Stable Coalition Matching for Fostering Multi-Agent Cooperation, arXiv e-prints, arXiv:2405.18044.
  4. Fan, T., Gu, H., Cao, X., Chan, C. S., Chen, Q., Chen, Y., Feng, Y., Gu, Y., Geng, J., Luo, B., Liu, S., Ong, W. K., Ren, C., Shao, J., Sun, C., Tang, X., Tae, H. X., Tong, Y., Wei, S., Wu, F., Xi, W., Xu, M., Yang, H., Yang, X., Yan, J., Yu, H., Yu, H., Zhang, T., Zhang, Y., Zhang, X., Zheng, Z., Fan, L., and Yang, Q., "Ten challenging problems in federated foundation models", IEEE Transactions on Knowledge and Data Engineering, 2025.
  5. He, S., Tang, B., Zhang, B., Shao, J., Ouyang, X., Nugraha, D. N., and Luo, B., "Fedkit: Enabling cross-platform federated learning for android and ios", in IEEE INFOCOM 2024-IEEE conference on computer communications workshops (INFOCOM WKSHPS), 2024.
  6. Geng, J., Tang, B., Zhang, B., Shao, J., and Luo, B., "FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analytics", in Proceedings of the Twenty-Fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, 2024.
  7. Shao, J., Han, S., He, C., and Luo, B., "Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data", in Workshop on Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities, in Conjunction with ICML 2023 (FL-ICML' 23), Jul. 2023.

Projects

MASArena: Benchmarking Framework for Multi-Agent Systems

  • MASArena is an open-source, modular benchmarking framework for single- and multi-agent systems, co-developed by DKU-Edge Intelligence Lab and Westlake University LINs-Lab.
  • Features plug-and-play modules, built-in benchmarks, visual debugging, and easy agent/tool/dataset integration.
  • Supports academic experiment reproduction, agent comparison, and toolchain evaluation.
  • Open-source and actively maintained. GitHub link

FedKit: Enabling Cross-Platform Federated Learning for Android and iOS

  • We present FEDKIT, which pipelines Cross-Platform FL for Android and iOS development by enabling model conversion, hardware-accelerated training, and cross-platform model aggregation.
  • Our workflow supports flexible federated learning operations (FLOps) in production, facilitating continuous model delivery and training.
  • This is a collaborative project with Prof. Luo, DKU undergraduate students Sichang He (lead), Beilong Tang, and Boyan Zhang, as well as collaborators Xiaomin Ouyang (UCLA) and Daniel Nata (Flower).
  • Our work has been ACCEPTED at IEEE INFOCOM 2024 Demo 🎉.
FedKit Model FedKit
FedKit Pipeline Overview FedKit Simulation

FedCampus: A Privacy-Preserving Data Platform for Smart Campus

  • We’re excited to announce the launch of the FedCampus Project - a privacy-preserving smart campus application, available on Android and iOS. 🎉 Video online available.
  • This app implements two key privacy-preserving technologies: Federated Learning and Differential Privacy. Check out our 100 customized smart watches for participants at DKU and FedCampus APP.
  • This is a collaborative project with Prof. Luo and DKU undergraduate students.
FedCampus

Edge-based Cross-device Federated Learning Prototypes

  • Our prototype supports Mobile and IoT devices operating at WiFi and USRP-based 4G/5G wireless networks.
  • This is a collaborative project with Prof. Luo and students from CUHKSZ
sys iot

Teaching Assistant

  • ELEC3120 - Computer Communication Networks (HKUST, Spring 2024)

Patents

  • B. Luo, J. Shao, Method and Apparatus for Online Parameter Selection in Minimizing the Total Cost of Federated Learning, CN202310485067.8, Apr. 2023, field
  • B. Luo, J. Shao, Method and Apparatus for Online Client Sampling in Minimizing the Training time of Federated Learning, CN 202310484383.3, Apr. 2023, field
  • B. Luo, J. Shao, J. Huang, Method and Apparatus for Frequent Items Mining Using Federated Analytics, CN202310365167.7, Mar. 2023, field
  • B. Luo, J. Shao, J. Huang, Method and Apparatus for Frequent Data Mining Based on Hierarchical Federated Analytics, CN202310330791.3, Mar. 2023, field