Introducing edge intelligence to smart meters via federated split learning.

ABSTRACT: The ubiquitous smart meters are expected to be a central feature of future smart grids because they enable the collection of massive amounts of fine-grained consumption data to support demand-side flexibility. However, current smart meters are not smart enough. They can only perform basic data collection and communication functions and cannot carry out on-device intelligent data analytics due to hardware constraints in terms of memory, computation, and communication capacity. Moreover, privacy concerns have hindered the utilization of data from distributed smart meters. Here, we present an end-edge-cloud federated split learning framework to enable collaborative model training on resource-constrained smart meters with the assistance of edge and cloud servers in a resource-efficient and pri

SUBMITTER: Li Y 

PROVIDER: S-EPMC11490544 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature