The first workshop on Principles Of Distributed Learning will be help on July 25 in conjunction with PODC 2025 ( 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] 



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