D'M'L 2017 - Workshop on Decentralized Machine Learning, Optimization and Privacy
Topics/Call fo Papers
The workshop will focus on decentralized machine learning and related challenges regarding distributed systems, optimization and privacy. With the advent of personal devices with computation and storage capabilities, it becomes possible to run machine learning on-device to provide personalized services to users without exposing their sensitive data to large data centers. Such decentralized architectures allow individuals to better control their data and to reduce the infrastructure costs and risks for the service provider, which motivates the design of machine learning and optimization algorithms adapted to constraints arising from this new paradigm. Beyond standard parallel computing techniques, it requires efficient solutions to deal with settings involving a very large number of parties, limited control over the network dynamics, heterogeneous local data distributions and/or the absence of a central coordinating entity. Another challenge is to develop decentralized learning protocols which provably preserve privacy for each user and show some robustness against malicious parties. The workshop will be devoted to the new challenges raised by decentralized machine learning, including:
- How to design efficient optimization algorithms (in terms of convergence rate, number of rounds, bandwidth, energy...) for the decentralized setting?
- How can users collaborate to learn useful models in a fully decentralized network where communication is peer-to-peer only?
- How to address privacy and security issues under various adversary models?
A major objective of the workshop is to initiate new fruitful collaborations between researchers in optimization, machine learning, privacy and distributed systems. Attendees are welcome to bring a poster to present their recent work.
Confirmed speakers (more to come):
- Borja Balle (Amazon Research)
- Keith Bonawitz (Google Research)
- Rachid Guerraoui (EPFL)
- Hamed Haddadi (QMUL)
- Mikael Johansson (KTH)
- Peter Richtárik (University of Edinburgh)
- Meilof Veeningen (Philips)
*Registration is free but mandatory*. Please register before *June 30* as we may not be able to guarantee attendance for late registrations. For more information and registration form, please visit:
https://team.inria.fr/magnet/workshop-on-decentral...
On behalf of the organizing committee:
Aurélien Bellet (INRIA)
Morten Dahl (Snips)
Sébastien Gambs (UQAM)
George Giakkoupis (INRIA)
Joseph Salmon (Télécom ParisTech)
- How to design efficient optimization algorithms (in terms of convergence rate, number of rounds, bandwidth, energy...) for the decentralized setting?
- How can users collaborate to learn useful models in a fully decentralized network where communication is peer-to-peer only?
- How to address privacy and security issues under various adversary models?
A major objective of the workshop is to initiate new fruitful collaborations between researchers in optimization, machine learning, privacy and distributed systems. Attendees are welcome to bring a poster to present their recent work.
Confirmed speakers (more to come):
- Borja Balle (Amazon Research)
- Keith Bonawitz (Google Research)
- Rachid Guerraoui (EPFL)
- Hamed Haddadi (QMUL)
- Mikael Johansson (KTH)
- Peter Richtárik (University of Edinburgh)
- Meilof Veeningen (Philips)
*Registration is free but mandatory*. Please register before *June 30* as we may not be able to guarantee attendance for late registrations. For more information and registration form, please visit:
https://team.inria.fr/magnet/workshop-on-decentral...
On behalf of the organizing committee:
Aurélien Bellet (INRIA)
Morten Dahl (Snips)
Sébastien Gambs (UQAM)
George Giakkoupis (INRIA)
Joseph Salmon (Télécom ParisTech)
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Last modified: 2017-05-31 23:19:37