Shohei Kamiguchi

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Demo Data from VLDB 2026 / reviewers in the wild / expert

dblp:440/2605 · DBLP profile ↗

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1ranked-venue papers

1as first author

1since 2021 · last 2026

—ORCID · none

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

Computer networks · 1 · 1 first-author · 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.

Artificial intelligence 1 paper		Efficient and distributed learning · 87% Reinforcement learning · 13%
Network and information security 1 paper		Privacy and data protection · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Efficient and distributed learning federated learning	1.0	1	2026	Communication-Efficient and Privacy-Preserving Cooperative Robot-Arm Imitation Learning via Parallel Split Federated Learning · INFOCOM 2026
Machine learning › Efficient and distributed learning › federated learning › federated learning architecture split federated learning	1.0	1	2026	Communication-Efficient and Privacy-Preserving Cooperative Robot-Arm Imitation Learning via Parallel Split Federated Learning · INFOCOM 2026
Machine learning › Reinforcement learning imitation learning	0.3	1	2026	Communication-Efficient and Privacy-Preserving Cooperative Robot-Arm Imitation Learning via Parallel Split Federated Learning · INFOCOM 2026
Privacy and data protection privacy-preserving machine learning	0.3	1	2026	Communication-Efficient and Privacy-Preserving Cooperative Robot-Arm Imitation Learning via Parallel Split Federated Learning · INFOCOM 2026

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

split federated learning · 2.0imitation learning · 2.0

Year	Publication	Venue	Position
2026	Communication-Efficient and Privacy-Preserving Cooperative Robot-Arm Imitation Learning via Parallel Split Federated Learning Shohei Kamiguchi, Takayuki Nishio	INFOCOM	1