Data privacy and security have become a non-negligible factor in load
forecasting. Previous researches mainly focus on training stage enhancement.
However, once the model is trained and deployed, it may need to `forget' (i.e.,
remove the impact of) part of training data if the data is found to be
malicious or as requested by the data owner. This paper introduces machine
unlearning algorithm which is specifically designed to remove the influence of
part of the original dataset on an already trained forecaster. However, direct
unlearning inevitably degrades the model generalization ability. To balance
between unlearning completeness and performance degradation, a
performance-aware algorithm is proposed by evaluating the sensitivity of local
model parameter change using influence function and sample re-weighting.
Moreover, we observe that the statistic criterion cannot fully reflect the
operation cost of down-stream tasks. Therefore, a task-aware machine unlearning
is proposed whose objective is a tri-level optimization with dispatch and
redispatch problems considered. We theoretically prove the existence of the
gradient of such objective, which is key to re-weighting the remaining samples.
We test the unlearning algorithms on linear and neural network load forecasters
with realistic load dataset. The simulation demonstrates the balance on
unlearning completeness and operational cost. All codes can be found at
this https URL

该论文介绍了一种专门设计用于在已经训练好的预测器上移除原始数据集中影响的机器遗忘算法，提出了一个性能感知算法来权衡遗忘完整性和性能衰减，并且还提出了一个任务感知的机器遗忘算法来考虑调度和再调度问题，通过模拟验证了遗忘算法在负载预测器上的性能。

任务感知的机器遗忘及其在负荷预测中的应用

Task-Aware Machine Unlearning and Its Application in Load Forecasting

The right to be forgotten has been legislated in many countries, but its
enforcement in the AI industry would cause unbearable costs. When single data
deletion requests come, companies may need to delete the whole models learned
with massive resources. Existing works propose methods to remove knowledge
learned from data for explicitly parameterized models, which however are not
appliable to the sampling-based Bayesian inference, i.e., Markov chain Monte
Carlo (MCMC), as MCMC can only infer implicit distributions. In this paper, we
propose the first machine unlearning algorithm for MCMC. We first convert the
MCMC unlearning problem into an explicit optimization problem. Based on this
problem conversion, an {\it MCMC influence function} is designed to provably
characterize the learned knowledge from data, which then delivers the MCMC
unlearning algorithm. Theoretical analysis shows that MCMC unlearning would not
compromise the generalizability of the MCMC models. Experiments on Gaussian
mixture models and Bayesian neural networks confirm the effectiveness of the
proposed algorithm. The code is available at
https://github.com/fshp971/mcmc-unlearning.

研究讨论在人工智能领域中实施遗忘权利将会引起难以承受的成本问题，该论文提出了用于 Markov chain Monte Carlo （简称 MCMC）的机器遗忘算法，第一次将 MCMC 反向推导为明确的最优化问题，通过 MCMC 引导函数确定遗忘的知识来达到遗忘的目的，并证明该算法不会损害 MCMC 模型的泛化能力。