30 May
2024
30 May
'24
3 p.m.
日本データベース学会の皆様
いつもお世話になっております。東工大の曹と申します。
来週の木曜日（6月6日）に、サイモンフレーザー大学（SFU）のケイ・ワン（Ke Wang）教授による東工大で講演が行われます。
ケイ・ワン教授はデータマイニング分野の著名な研究者で、特にプライバシーを保護するデータマイニングに関する専門書で知られています。
今回の東京工業大学での講演テーマはフェデレーテッドラーニングに関連しています。
Zoomと東工大の現地会場でハイブリッド形式で開催しますので、皆様ぜひ積極的にご参加ください。
Prof. Ke Wang from Simon Fraser University (SFU) will give a lecture next
Thursday (June 6, 13:00-14:00).
Prof. Ke Wang is a well-known researcher in the field of data mining and is
renowned for his technical book on privacy-preserving data publishing. The
topic of his talk at Tokyo Tech is related to Federated Learning.
The talk will be given hybrid via Zoom and on-site, so everyone is
encouraged to attend.
*日時：* *2024/6/6 13:00-14:00 *
*ZOOM: https://zoom.us/j/94209664502
<https://zoom.us/j/94209664502> (maximum 300 participants)*
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*Talk Title: *
Differentially Private Machine Learning for Decentralized and Time-evolving
Data
*Abstract:*
Decentralized machine learning focuses on learning from data distributed at
multiple related sites, where due to privacy or regulatory concerns, data
pooling is not an option.
In contrast to the real-world requirements, current methods in
decentralized machine learning, notably federated learning, force
participating sites into tight collaboration where the sites are forced
into symmetric sharing and shared decision making. That is, all sites have
to contribute their data to benefit from the learning process, and have to
share the same model types, architectures, training methodologies, feature
and sample spaces, etc. The issues are compounded in the case of
privacy-preservation and time-evolving data streams, where the sites have
to agree on a common, one-size-fits-all privacy budget, and the continuous
model updates required for handling time-evolving data streams erode the
privacy budget, deteriorating utility. Forced tight collaboration creates
barriers to participation where participating sites want to benefit from
other sites’ data but do not wish to share their own information or change
the existing data analysis practices.
In this work, we propose an end-to-end solution for differentially private
decentralized learning. Where our first contribution is PubSub-ML, a
differentially private, decentralized learning framework under loose
collaboration for static data. Proposed as an alternative to federated
learning, PubSub-ML allows the participating sites to maintain autonomy on
all decisions related to their learning processes. Our second contribution
is DP-Ensemble, a differentially private, dynamic model integration
approach for a single site that allows unlimited model updates for
time-evolving data streams on a fixed privacy budget. Our third
contribution extends PubSub-ML to data streams using DP-Ensemble, allowing
differentially private, decentralized modeling of data streams under loose
collaboration and a fixed privacy budget. All contributions are supported
by extensive empirical evaluation.
*Speaker*: Professor Ke Wang, Simon Fraser University
*Bio*:
Professor Ke Wang received Ph.D from Georgia Institute of Technology. He is
currently a professor at School of Computing Science, Simon Fraser
University. Ke Wang's research interests include database technology, data
mining and knowledge discovery, with emphasis on massive datasets, graph
and network data, and data privacy. He co-authored a book "Introduction to
Privacy-Preserving Data Publishing: Concepts and Techniques", Data Mining
and Knowledge Discovery Series, Chapman & Hall/CRC, August 2010.
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どうぞよろしくお願いいたします。
曹 洋