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Boyu Dong

dblp:305/9411 · DBLP profile ↗
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6ranked-venue papers
1as first author
6since 2021 · last 2026
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 2 since 2021Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021Theory of computation · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Hardware security and side channels · 100%
Computer networks
1 paper
Cellular and mobile networks · 77% Physical-layer communications · 23%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Integrated circuit design · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cellular and mobile networks › 6g
terahertz communication
0.912025
Nonlinear behaviors of transceivers for terahertz communications: data sets and models · Sci. China Inf. Sci. 2025
Hardware security and side channels › side-channel attack
electromagnetic side channel
0.512021
Tamper-Resistant Optical Logic Circuits Based on Integrated Nanophotonics · DAC 2021
Hardware security and side channels
side-channel attack
0.512021
Tamper-Resistant Optical Logic Circuits Based on Integrated Nanophotonics · DAC 2021
Integrated circuit design › photonic integrated circuits
photonic integrated circuit design
0.512021
Tamper-Resistant Optical Logic Circuits Based on Integrated Nanophotonics · DAC 2021
Physical-layer communications
channel modeling
0.312025
Nonlinear behaviors of transceivers for terahertz communications: data sets and models · Sci. China Inf. Sci. 2025
Hardware security and side channels
tamper-resistant hardware
0.112021
Tamper-Resistant Optical Logic Circuits Based on Integrated Nanophotonics · DAC 2021

Methods — techniques the papers use, named apart from their topics

phase modulation · 1.0electronically-controlled phase shifter · 1.0
YearPublicationVenuePosition
2026 Medium-term prediction of atmospheric PM2.5 concentration based on the VG-TCABI hybrid architecture
Boqun Li, Boyu Dong, Jihan Li, Guangtao Fan, Feng Yu 0008, Jin Zhang 0028
Neural Networks2
2026 Temporal Liveness Enforcement via Parameter Tuning in Dual-Time Petri Nets
Ruotian Liu, Yufeng Chen 0001, Maria Pia Fanti, Ding Liu 0001, Boyu Dong
IEEE Trans Autom. Sci. Eng.6
2025 Dead Transitions Analysis and Resolution in Dual-Time Petri Nets
Ruotian Liu, Yufeng Chen 0001, Maria Pia Fanti, Boyu Dong
VECoS6
2025 Nonlinear behaviors of transceivers for terahertz communications: data sets and models
Kai Ying, Pengxuan Gao, Linshan Zhao, Yinjun Liu, Boyu Dong, Junwen Zhang 0001
Sci. China Inf. Sci.6
2025 FADngs: Federated Learning for Anomaly Detection
abstract
With the increasing demand for data privacy, federated learning (FL) has gained popularity for various applications. Most existing FL works focus on the classification task, overlooking those scenarios where anomaly detection may also require privacy-preserving. Traditional anomaly detection algorithms cannot be directly applied to the FL setting due to false and missing detection issues. Moreover, with common aggregation methods used in FL (e.g., averaging model parameters), the global model cannot keep the capacities of local models in discriminating anomalies deviating from local distributions, which further degrades the performance. For the aforementioned challenges, we propose Federated Anomaly Detection with Noisy Global Density Estimation, and Self-supervised Ensemble Distillation (FADngs). Specifically, FADngs aligns the knowledge of data distributions from each client by sharing processed density functions. Besides, FADngs trains local models in an improved contrastive learning way that learns more discriminative representations specific for anomaly detection based on the shared density functions. Furthermore, FADngs aggregates capacities by ensemble distillation, which distills the knowledge learned from different distributions to the global model. Our experiments demonstrate that the proposed method significantly outperforms state-of-the-art federated anomaly detection methods. We also empirically show that the shared density function is privacy-preserving. The code for the proposed method is provided for research purposes https://github.com/kanade00/Federated_Anomaly_detection.
Boyu Dong, Dong Chen 0017, Yu Wu 0011, Siliang Tang, Yueting Zhuang
IEEE Trans. Neural Networks Learn. Syst.1
2021 Tamper-Resistant Optical Logic Circuits Based on Integrated Nanophotonics
abstract
A tamper-resistant logical operation method based on integrated nanophotonics is proposed focusing on electromagnetic side-channel attacks. In the proposed method, only the phase of each optical signal is modulated depending on its logical state, which keeps the power of optical signals in optical logic circuits constant. This provides logic-gate-level tamper resistance which is difficult to achieve with CMOS circuits. An optical implementation method based on electronically-controlled phase shifters is then proposed. The electrical part of proposed circuits achieves 300 times less instantaneous current change, which is proportional to intensity of the leaked electromagnetic wave, than a CMOS logic gate.
Jun Shiomi, Shuya Kotsugi, Boyu Dong, Hidetoshi Onodera, Akihiko Shinya, Masaya Notomi
DAC3