1st Workshop on Distributed Machine Learning, co-located with CoNEXT 2020

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Machine Learning and Deep Neural Networks are gaining more and more traction in a range of tasks 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 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 collaborative learning tasks. More specifically, we welcome papers in the following areas:

• Distributed inference and offloading
• DNN computation sharing in local networks
• Distributed Learning
• Federated Learning
• Distributed and asynchronous training algorithms
• Channel optimisations for distributed ML
• DNN based compression schemes
• Fairness and biases in federated learning
• Novel ML applications in IoT, MEC or NFV
• Secure and privacy-preserving distributed learning

The workshop is co-located with CoNEXT in the beautiful - and sunny - city of Barcelona, in a region close to the beach. 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 1st of December 2020.

Deadline for paper submissions: 11th September 2020 18th September 2020

Program How to get there Call for Papers Poster