15 citations · 27 across the 7 of their papers we have counts for
8 papers
Global Convolutional Neural Processes
Xuesong Wang, Lina Yao, Xianzhi Wang +2
The ability to deal with uncertainty in machine learning models has become equally, if not more, crucial to their predictive ability itself. For instance, during the pandemic, gove…
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…
Simeon -- Secure Federated Machine Learning Through Iterative Filtering
Nicholas Malecki, Hye-young Paik, Aleksandar Ignjatovic +2
Federated learning enables a global machine learning model to be trained collaboratively by distributed, mutually non-trusting learning agents who desire to maintain the privacy of…
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…
Design Patterns for Blockchain-based Self-Sovereign Identity
Yue Liu, Qinghua Lu, Hye-Young Paik +1
Self-sovereign identity is a new identity management paradigm that allows entities to really have the ownership of their identity data and control their use without involving any i…