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4th International Workshop on Distributed Machine Learning, co-located with CoNEXT 2023
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 datacenters 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. Moreover, distributed ML can work as an enabler for various use-cases previously considered unattainable only using local resources.
Be it in a distributed environment, such as a datacenter, 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 optimizing collaborative and distributed learning tasks. More specifically, we aim to welcome papers in the following areas:
- Distributed inference and offloading
- Efficient DNN inference frameworks
- Efficient training/inference for large generative foundation models
- DNN computation sharing in local networks
- DNN-based compression schemes
- Distributed and asynchronous training algorithms
- Channel optimisations for distributed ML
- Federated and collaborative Learning
- Fairness and biases in federated learning
- Security and privacy in distributed learning
- Interpretability in distributed/collaborative learning
- Training and deployment of hyperscale models
- Node heterogeneity and stragglers in distributed ML
- Novel ML applications in IoT, MEC, SDN or NFV scenarios
This year, we want to lay a special emphasis on an area that has changed the computational landscape and is of specific interest in the area of DistributedML, the field of training and deployment of large generative foundation models. We believe that these are key aspects becoming increasingly important to solve for successful, tractable and sustainable distributed deployments in the wild.
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’23 will be held in the beautiful city of Paris. 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 8th of December 2023.
Deadline for paper submissions: 15th September 2023 22nd September 2023
