670 citations · 1.6k across the 36 of their papers we have counts for
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Decision Models for Selecting Federated Learning Architecture Patterns
Sin Kit Lo, Qinghua Lu, Hye-Young Paik +1
Federated machine learning is growing fast in academia and industries as a solution to solve data hungriness and privacy issues in machine learning. Being a widely distributed syst…
Cycle-Balanced Representation Learning For Counterfactual Inference
Guanglin Zhou, Lina Yao, Xiwei Xu +2
With the widespread accumulation of observational data, researchers obtain a new direction to learn counterfactual effects in many domains (e.g., health care and computational adve…
Blockchain-based Trustworthy Federated Learning Architecture
Sin Kit Lo, Yue Liu, Qinghua Lu +4
Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organisations or devices) train models locally and formulate a global model based on the loca…
FLRA: A Reference Architecture for Federated Learning Systems
Sin Kit Lo, Qinghua Lu, Hye-Young Paik +1
Federated learning is an emerging machine learning paradigm that enables multiple devices to train models locally and formulate a global model, without sharing the clients' local d…
Generating Informative CVE Description From ExploitDB Posts by Extractive Summarization
Jiamou Sun, Zhenchang Xing, Hao Guo +4
ExploitDB is one of the important public websites, which contributes a large number of vulnerabilities to official CVE database. Over 60\% of these vulnerabilities have high- or cr…
Architectural Patterns for the Design of Federated Learning Systems
Sin Kit Lo, Qinghua Lu, Liming Zhu +3
Federated learning has received fast-growing interests from academia and industry to tackle the challenges of data hungriness and privacy in machine learning. A federated learning…