In view of recent COVID-19 circumstances, the CoNEXT conference committee has decided to move the conference fully virtual.
As such, all workshops will also be held online. More information to follow, but also keep updated with the CoNEXT’20 website.
Call for Papers
Machine learning has been tremendously successful in enabling ubiquitous smart applications that facilitate people’s everyday life. The training and inference of machine learning models have traditionally taken place on a centralised cloud facilities. Running machine learning models on the cloud, however, is associated with high rental/operational-cost, latency introduced by networks that is often hardly predictable, as well as the potential risk of compromising user’s data privacy. Recent advancement in computational power at the mobile edge, including smart consumer devices such as mobile phones, tablets, and smart watches, have made it possible to execute machine learning models partially or entirely on device. A more ambitious endeavour, that has already proven feasible, is to train or partially train the model on devices. Distributed inference and training on a plural of geographically separated devices with diverse computation capacities and network qualities are challenging topics that require research effort and discussions, to push forward the advancement in these areas.
The 1st edition of DistributedML workshop at CoNEXT’2020 will serve as a forum for networking and AI researchers to discuss the challenging topics, share new ideas, and exchange experiences across the areas of networking and distributed AI, from both theoretical and experimental aspects. We warmly invite submission of original, previously unpublished papers addressing key issues in distributed machine learning, specifically in areas including, but not limited to:
Distributed inference and offloading
DNN computation sharing in local networks
Distributed and asynchronous training algorithms
Channel optimisations for distributed learning
DNN based Compression schemes
Fairness and biases in federated learning
Novel ML applications in IoT, MEC or NFV
Secure and privacy-preserving distributed learning
Solicited submissions include both full technical workshop papers and white paper position papers. Maximum length of such submissions is 6 pages (excluding references) in 2-column 10pt ACM format.
All the submissions should be double-blind and will be peer-reviewed. For anonymity purposes, you must remove any author names and other uniquely identifying features in your submitted paper.