The first workshop on Principles Of Distributed Learning will be help on July 25 in conjunction with PODC 2025 (https://www.podc.org) in beautiful Salerno

The workshop will be informal and will include the following talks:

  *   Hagit Attiya (Technion), Asynchronous distributed machine learning
  *   Ce Zhang (ETH Zurich), Scaling up distributed learning with system relaxations: bagua and beyond
  *   Alonso Gustavo (ETH Zurich), The role of hardware in distributed learning and its impact on algorithms
  *   Nitin Vaidya (Georgetown University), Some open problems in fault-tolerant distributed optimization and learning
  *   Nirupam Gupta (EPFL), The crucial role of momentum in byzantine learning
  *   Suhas Diggavi (UCLA),  On privacy and security in federated learning
  *   Rafael Pinot (EPFL), Can Byzantine learning be private?
  *   Dan Alistarh (IST),  Elastic consistency: a general consistency model for distributed optimization
  *   Ji Liu (Stony Brook University), Distributed multi-armed bandits
  *   Waheed Bajwa (Rutgers University),  Fast and exact distributed principal component analysis
  *   Anne-marie Kermarrec (EPFL), Frugal distributed learning
  *   Nir Shavit (MIT), Tissue vs silicon: musings on the future of deep learning hardware and software
  *   Indranil Gupta (UIUC),  Hammer or gavel. or how i learnt to stop learning and love the old-fashioned algorithm

If you are interested in participating, send a message to [log in to unmask] and [log in to unmask]

R

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