Privacy-Preserving Distributed Learning for Residential Short-Term Load
  Forecasting
Yi Dong Yingjie Wang Mariana Gama Mustafa A. Mustafa Geert Deconinck Xiaowei Huang
In the realm of power systems, the increasing involvement of residential
users in load forecasting applications has heightened concerns about data
privacy. Specifically, the load data can inadvertently reveal the daily
routines of residential users, thereby posing a risk to their property
security. While federated learning (FL) has been employed to safeguard user
privacy by enabling model training without the exchange of raw data, these FL
models have shown vulnerabilities to emerging attack techniques, such as Deep
Leakage from Gradients and poisoning attacks. To counteract these, we initially
employ a Secure-Aggregation (SecAgg) algorithm that leverages multiparty
computation cryptographic techniques to mitigate the risk of gradient leakage.
However, the introduction of SecAgg necessitates the deployment of additional
sub-center servers for executing the multiparty computation protocol, thereby
escalating computational complexity and reducing system robustness, especially
in scenarios where one or more sub-centers are unavailable. To address these
challenges, we introduce a Markovian Switching-based distributed training
framework, the convergence of which is substantiated through rigorous
theoretical analysis. The Distributed Markovian Switching (DMS) topology shows
strong robustness towards the poisoning attacks as well. Case studies employing
real-world power system load data validate the efficacy of our proposed
algorithm. It not only significantly minimizes communication complexity but
also maintains accuracy levels comparable to traditional FL methods, thereby
enhancing the scalability of our load forecasting algorithm.