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Introducing edge intelligence to smart meters via federated split learning.

Yehui Li1, Dalin Qin1, H Vincent Poor2

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This study introduces a federated split learning framework for smart meters, enabling on-device analytics despite hardware limits. This approach enhances demand-side flexibility and privacy in smart grids.

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Area of Science:

  • Smart Grid Technology
  • Artificial Intelligence
  • Data Analytics

Background:

  • Smart meters are crucial for smart grids, collecting consumption data for demand-side flexibility.
  • Current smart meters lack on-device analytics due to hardware constraints (memory, computation, communication).
  • Privacy concerns limit the use of distributed smart meter data.

Purpose of the Study:

  • To develop an end-edge-cloud federated split learning framework for smart meters.
  • To enable intelligent data analytics on resource-constrained smart meters.
  • To ensure resource efficiency and privacy in collaborative model training.

Main Methods:

  • Implemented a federated split learning framework integrating edge and cloud servers.
  • Validated the framework on a hardware platform with smart meters having 192 KB SRAM.
  • Conducted building and household load forecasting using the proposed method.

Main Results:

  • Reduced memory footprint by 95.5% and training time by 94.8%.
  • Decreased communication burden by 50% within the distributed learning framework.
  • Achieved comparable or superior load forecasting accuracy to conventional methods.

Conclusions:

  • The proposed federated split learning framework effectively enables intelligent analytics on resource-constrained smart meters.
  • The method addresses hardware limitations and privacy concerns, enhancing smart grid capabilities.
  • This approach offers a viable solution for efficient and private data utilization in smart grids.