22 citations · 39 across the 3 of their papers we have counts for
5 papers
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…
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…
Dynamic Fusion based Federated Learning for COVID-19 Detection
Weishan Zhang, Tao Zhou, Qinghua Lu +6
Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, sharing diagnostic images…
Blockchain-based Federated Learning for Device Failure Detection in Industrial IoT
Weishan Zhang, Qinghua Lu, Qiuyu Yu +6
Device failure detection is one of most essential problems in industrial internet of things (IIoT). However, in conventional IIoT device failure detection, client devices need to u…