The new paradigm of edge computing is expected to improve the agility of HPC platforms and cloud service deployments by using opportunistic local computing resources. Implementing or analysing new types of data intensive applications close to the place where the data are produced is crucial for designing efficient distributed machine learning methods and as a consequence, reducing the data movements that consume most of the energy.
The challenges are to design new machine learning methods that fully exploit the distributed character of the edge and to develop algorithms and subsequent pieces of software that will allow the deployment of the edge/fog hybrid infrastructures.
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