J
Jihong Park
Researcher at Deakin University
Publications - 153
Citations - 4560
Jihong Park is an academic researcher from Deakin University. The author has contributed to research in topics: Computer science & Cellular network. The author has an hindex of 26, co-authored 136 publications receiving 2763 citations. Previous affiliations of Jihong Park include University of Delaware & Korea University.
Papers
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Wireless Network Intelligence at the Edge
TL;DR: In this article, the key building blocks of edge ML, different neural network architectural splits and their inherent tradeoffs, as well as theoretical and technical enablers stemming from a wide range of mathematical disciplines are presented.
Journal ArticleDOI
Blockchained On-Device Federated Learning
TL;DR: In this article, a blockchained federated learning (BlockFL) architecture is proposed, where local learning model updates are exchanged and verified by utilizing a consensus mechanism in blockchain.
Journal ArticleDOI
Wireless Access for Ultra-Reliable Low-Latency Communication: Principles and Building Blocks
Petar Popovski,Jimmy Jessen Nielsen,Cedomir Stefanovic,Elisabeth de Carvalho,Erik G. Ström,Kasper Floe Trillingsgaard,Alexandru-Sabin Bana,Dong Min Kim,Radoslaw Kotaba,Jihong Park,René Brandborg Sørensen +10 more
TL;DR: The principles for supporting URLLC are discussed from the perspective of the traditional assumptions and models applied in communication/information theory, and how these principles are applied in various elements of system design, such as use of various diversity sources, design of packets, and access protocols.
Posted Content
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data.
TL;DR: Federated distillation (FD) is proposed, a distributed model training algorithm whose communication payload size is much smaller than a benchmark scheme, federated learning (FL), particularly when the model size is large.
Posted Content
Wireless Network Intelligence at the Edge
TL;DR: In a first of its kind, this article explores the key building blocks of edge ML, different neural network architectural splits and their inherent tradeoffs, as well as theoretical and technical enablers stemming from a wide range of mathematical disciplines.