NTT is developing an asynchronous distributed deep learning technology based on machine learning at the network edge.
NTT’s research is focused on a training algorithm to create a global model of IoT data that is processed and stored at the edge.
NTT says its proposed technology, which has both academic and practical interest, enables to obtain a global model (a trained model that uses all the data at a single place) even when (1) statistically nonhomogeneous data subsets are placed on multiple servers, and (2) the servers only asynchronously exchange variables related to the model.
Details will be presented this week at KDD 2020 (Knowledge Discovery and Data Mining), an international conference sponsored by the Association for Computing Machinery (ACM) (16.9% acceptance rate). We also published the code associated with our achievement on Github for verification of our method effectiveness.
https://www.ntt.co.jp/news2020/2008e/200824a.html
