NTT develops distributed deep learning for edge computing

"Our research is investigating a training algorithm to obtain a global model as if it is trained by aggregating data in a single server, even when the data are placed in distributed servers, such as in edge computing," according to the statement. NTT's proposed technology has enabled developers to successfully train a global model in early experiments-even in cases where different types of data are used and the communication between servers is "asynchronous," meaning that each compute node's results are not dependent on receiving data and results from another node. NTT notes that interest in edge computing is growing because of the benefits for lower application latency, and expects that there will be community interest in the application of its research to edge compute and networking services. The company said it will continue to develop the technology for commercial applications, and will release the source code to promote collaboration.