Young Scholar TechTalk – Subgraph Federated Learning with Missing Neighbor Generation

All members of the HKU community and the general public are welcome to join!
Speaker: Miss Zhang Ke, PhD Candidate, Department of Computer Science, HKU
Date: 11th October 2022 (Tuesday)
Time: 4:30pm
Mode: Mixed
About the Tech Talk
All members of the HKU community and the general public are welcome to join!
Speaker: Miss Zhang Ke, PhD Candidate, Department of Computer Science, HKU
Moderator: Miss Zeng Te, PhD Candidate, Department of Computer Science, HKU
Date: 11th October 2022 (Tuesday)
Time: 4:30pm
Mode: Mixed (both face-to-face and online). Seats for on-site participants are limited. A confirmation email will be sent to participants who have successfully registered.
Language: English

In computer science, a graph is a network modeling objects and their unique interactions. The graph learning model is a specialized machine learning model that learns on graphs. Similar to traditional machine learning models, a well-performed graph learning model can capture the global data distribution with sufficient and unbiased training data. However, in a distributed subgraph system, most data owners only possess small amounts of the data (small subgraphs) in their local systems and can have unpredictable biases.
In this talk, the speaker will introduce this novel yet realistic setting – subgraph federated learning, which aims to let distributed data owners collaboratively train a powerful and generalized graph learning model without directly sharing their subgraphs. Towards this setting, two major techniques are proposed by the research team. (1) FedSage, which trains a GraphSage model based on FedAvg to integrate node features, link structures, and task labels on multiple local subgraphs; (2) FedSage+, which trains a missing neighbor generator along FedSage to deal with missing links across local subgraphs. Empirical results and theoretical analysis of proposed models respectively demonstrate the effectiveness and prove the generalization ability.

  • The tech talk “Subgraph Federated Learning with Missing Neighbor Generation” will be organized in the Tam Wing Fan Innovation Wing Two (G/F, Run Run Shaw Building, HKU) on 11th October 2022 (Tuesday)4:30 pm.
  • Stay tuned for the registration.
  • Seats are limited. Zoom broadcast is available if the seating quota is full. 
  • Registrants on the waiting list will be notified of the arrangement after the registration deadline (with seating/free-standing/other arrangement)
  • Please read the Campus Access and HKU Vaccine Pass (
About the speaker

Miss Zhang Ke

Miss Zhang Ke is a fourth-year Ph.D. candidate majored in Computer Science at HKU, supervised by Prof. Siu Ming Yiu. Ke received her B.Sc. degree from Jilin University, China, in 2018. She was also a visiting scholar at Emory University, the United States, supervised by Dr. Carl Yang from 2021 to 2022. Ke’s research interests are federated learning, graph mining, and data privacy. She has published research results in top venues like NeurIPS, IJCAI, and ICML-FL. She also serves as the reviewer for the Journal of Big Data and top conferences such as WWW, KDD, MICCAI, and CIKM-FedGraph.

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