Workshop on Decentralized Machine Learning, 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 (with potential incentives for its usage), as well as reduce the infrastructure costs and risks related to data storage/processing for the service provider. This motivates the design of machine learning and optimization algorithms adapted to constraints arising from this new paradigm. Well beyond standard parallel computing techniques, it requires efficient solutions to deal with complex 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 important challenge is to develop decentralized learning protocols which provably preserve privacy for each user and show some robustness against malicious parties.

This multidisciplinary workshop will be devoted to the new crucial scientific 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 (no central entity)?
  • 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 at the poster session.

This content has been updated on 31 July 2017 at 16 h 52 min.