Communication efficient machine learning of data across multiple sites

#The present disclosure relates generally to computer networks, and, more particularly, to communication efficient machine learning of data across multiple sites.

#As the use of machine learning in computer networks continues to increase over time, the physical limitations of a network may present barriers to deployment. For example, consider the case in which a machine learning model is to be trained centrally using training data from any number of distributed locations. In this scenario, this can be achieved by transmitting the training data from all of the different locations to a central entity. However, doing so will also consume more network bandwidth which may be prohibitively costly or even infeasible, in some cases.

#Beyond the potential inability to leverage all available training data due to network constraints, the gathered data is also often only partially labeled. In general, labeling training data serves as examples during model training of the different classifications that can be applied to a new data sample. For example, in the context of image recognition, some images may be labeled as depicting a “car,” while other images may be labeled as depicting a “bus.” By doing so, the trained model may be able to distinguish between images that include a “car” vs. images that include a “bus.” However, in the real world, it is relatively rare to have fully labeled data to serve as a ground-truth for purposes of model training. Indeed, labeling data can be very labor intensive and, even when labeling is automated, not every potential data source may have requisite knowledge to label the data. For example, security camera images can be associated with badge reader information, to better label the images. However, not every camera may have a corresponding badge reader to leverage for purposes of data labeling.

#The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which:

#FIGS. 1A-1B illustrate an example communication network;

#FIG. 2 illustrates an example network device/node;

#According to one or more embodiments of the disclosure, a service receives machine learning-based generative models from a plurality of distributed sites. Each generative model is trained locally at a site using unlabeled data observed at that site to generate synthetic unlabeled data that mimics the unlabeled data used to train the generative model. The service receives, from each of the distributed sites, a subset of labeled data observed at that site. The service uses the generative models to generate synthetic unlabeled data. The service trains a global machine learning-based model using the received subsets of labeled data received from the distributed sites and the synthetic unlabeled data generated by the generative models.

#A computer network is a geographically distributed collection of nodes interconnected by communication links and segments for transporting data between end nodes, such as personal computers and workstations, or other devices, such as sensors, etc. Many types of networks are available, with the types ranging from local area networks (LANs) to wide area networks (WANs). LANs typically connect the nodes over dedicated private communications links located in the same general physical 1o location, such as a building or campus. WANs, on the other hand, typically connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), or synchronous digital hierarchy (SDH) links, or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, and others. The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol consists of a set of rules defining how the nodes interact with each other. Computer networks may further be interconnected by an intermediate network node, such as a router, to extend the effective “size” of each network.

#Smart object networks, such as sensor networks, in particular, are a specific type of network having spatially distributed autonomous devices such as sensors, actuators, etc., that cooperatively monitor physical or environmental conditions at different locations, such as, e.g., energy/power consumption, resource consumption (e.g., water/gas/etc. for advanced metering infrastructure or “AMI” applications) temperature, pressure, vibration, sound, radiation, motion, pollutants, etc. Other types of smart objects include actuators, e.g., responsible for turning on/off an engine or perform any other actions. Sensor networks, a type of smart object network, are typically shared-media networks, such as wireless networks. That is, in addition to one or more sensors, each sensor device (node) in a sensor network may generally be equipped with a radio transceiver or other communication port, a microcontroller, and an energy source, such as a battery. Often, smart object networks are considered field area networks (FANs), neighborhood area networks (NANs), personal area networks (PANs), etc. Generally, size and cost constraints on smart object nodes (e.g., sensors) result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth.