Zhiyuan Wu (Jerry)

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  • Zhiyuan Wu                                                                                                            

  • Address: No.6, Kexueyuan South Road, Zhongguancun, Haidian District, Beijing, China

  • Email: wuzhiyuan22s [at] ict.ac.cn; wuzhiyuan2000 [at] ieee.org

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"The greatest truths are the simplest."

About Me

         I'm currently a master’s candidate and a research assistant with the Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS). I am primarily supervised by Prof. Yuwei Wang, with collaborative guidance from Prof. Sheng Sun and Prof. Min Liu.

         I have a mixed background and experience in various fields, including computer architecture, network & security, artificial intelligence, etc. I am committed to developing project collaborations with all kinds of research entities, and have led or participated in 20+ research or engineering projects sponsored by national research institutions or major internet enterprises, including Chinese Academy of Sciences, China Mobile Communications, Huawei, etc. I have published/preprinted 10+ technical papers, including several technical papers published on top-tier conferences and journals as the first author in the fields of computer architecture, computer networks, and intelligent systems, including IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Mobile Computing (TMC), IEEE International Conference on Computer Communications (INFOCOM), and ACM Transactions on Intelligent Systems and Technology (TIST). Therein, the proposed FedCache pioneers cache-driven architecture over federated learning community and achieves two orders of magnitude in communication efficiency improvement compared with related state-of-the-art. I have served as a technical program committee member or a reviewer for 10+ international conferences and journals, represented by International Conference on Machine Learning (ICML), Advances in Neural Information Processing Systems (NeurIPS), International Conference on Learning Representations (ICLR), and IEEE Global Communications Conference (Globecom). I am a member of Institute of Electrical and Electronics Engineers (IEEE), Association for Computing Machinery (ACM), International Telecommunication Union (ITU) and China Computer Federation (CCF). I have been granted the President Special Prize (the highest award in ICT/CAS) and the National Scholarship.

News!!!

Selected Publications

Featured Publications

FedICT: Federated Multi-task Distillation for Multi-access Edge Computing.

Zhiyuan Wu, et al. TPDS.

We propose FedICT, a novel framework that resolves the trade-off between global model aggregation and personalized services in federated edge learning. FedICT enables end devices to perform multitask training in a communication-efficient and model-heterogeneous manner, and is suitable for multi-access edge computing.

FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence.

Zhiyuan Wu, et al. TMC.

We propose FedCache, the first cache-driven federated learning architecture that guarantees satisfactory performance while conforming to personalized devices-side limitations in edge computing. FedCache improves communication efficiency by up to 278.9 times over previous architectures and can accommodate heterogeneous devices and asynchronous interactions.

Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration.

Zhiyuan Wu, et al. INFOCOM.

We propose FedAgg, a novel hierarchical federated learning framework that leverages end-edge-cloud collaboration to perform recursive agglomeration with model-agnostic interaction protocols. FedAgg is the first framework to enable training larger models with ever-increasing capability tier by tier up to the cloud, and satisfy the privacy constraints of federated learning as well as the flexibility requirements of end-edge-cloud computing.

Selected Publications

Note: Bold & underlined names indicate that I or my directly supervised students are the first authors. An asterisk (*) denotes the corresponding author.

Selected Awards

During Postgraduate

During Undergraduate

Academic Service

Academic Organization Membership

Journal Reviewer

Conference Technical Program Committee/ Conference Reviewer

Selected Activities

Conference/ Symposium Suborganizer

Conference/ Symposium Participation

Invited Talks/ Technical Report

Competition/Award Subreferee

Professional Standards

Selected Projects

Featured Projects

MindSpore Network Model and Federated Innovation Collaboration.

Huawei Technologies Co. Ltd.

MindSpore is an open AI framework supported by Huawei Technologies Co. Ltd. that matches with Ascend processors and supports multi-processor architectures. Before Feb. 2023, MindSpore has gained more than 3.3k stars on github, and is awarded the World's 3rd Most Popular Federated Learning Framework, only inferior to PySyft and FATE. I was involved in integrating the server communication module with the asynchronous federated learning and client selection functional modules in the framework.

China's National Standard Acquisition.

Standardization Administration of the People's Republic of China.

China's national standard acquisition is to convert the content of international standards into China national standards after investigation, analysis, research and experimental verification. The converted international standards are approved and released according to Chinese regulations. I have participated in the sinicization of 3 IEEE professional standards related to computer networks and communications, including IEEE Std 802.1Qch-2017, IEEE Std 802.1Qci-2017 and IEEE Std 802.1Qbz-2016.

Federated Learning-based Intelligent Scheduling and Collaborative Optimization at the Edge of Cloud Networks.

Institute of Computing Technology, Chinese Academy of Sciences.

This project conducts prospective research on intelligent scheduling and collaborative optimization of federated learning based on end-edge-cloud collaboration, aiming to improve the resource utilization of computing networks under hierarchical architecture. I contributed to the application form, the development of an end-edge-cloud collaborative federated learning framework, and the interim report.

Selected Projects

Mentorship

Master Students

Undergraduate Students

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