DistributedML 2021 DistributedML 2021

2nd Workshop on Distributed Machine Learning, co-located with CoNEXT 2021


Machine Learning and Deep Neural Networks have been gaining more and more traction in a range of tasks across modalities, such as image recognition, text mining as well as ASR. Commonly represented as execution graphs, they can be found deployed in a range of devices, from large servers in data centers to small embedded devices, offering them intelligent capabilities. A common denominator between these diverse devices is that they are connected, thus enabling them to collaboratively work towards the learning or inference task.

Be it in a distributed environment, such as a data center, or a highly heterogeneous embedded deployment in the wild, distributed ML poses various challenges from a systems, interconnection and ML theoretical perspective. In this workshop, we want to focus on the network-side of such distributed setups, the unique challenges it presents and novel solutions for optimising distributed learning tasks. More specifically, we welcome papers in the following areas:

We hope that DistributedML will serve as a forum for researchers across different disciplines to bring forward and discuss challenging topics, share new ideas and exchange experience in the deployment of such systems, both from a theoretical and experimental perspective.

The workshop is co-located with CoNEXT will be virtually held in the beautiful Bavarian city of Munich. All accepted paper will also be included in the conference proceedings and be made available in the ACM Digital Library.

The workshop will take place on the 6th to 10th of December 2021.

Deadline for paper submissions: 17th September 2021

Call for Papers