Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting
Accurate load forecasting is crucial for energy management, infrastructure planning, and demand-supply balancing. Smart meter data availability has led to the demand for sensor-based load forecasting. Conventional ML allows training a single global model using data from multiple smart meters requiring data transfer to a central server, raising concerns for network requirements, privacy, and security. We propose a split learning-based framework for load forecasting to alleviate this issue. We split a deep neural network model into two parts, one for each Grid Station (GS) responsible for an entire neighbourhood's smart meters and the other for the Service Provider (SP). Instead of sharing their data, client smart meters use their respective GSs' model split for forward pass and only share their activations with the GS. Under this framework, each GS is responsible for training a personalized model split for their respective neighbourhoods, whereas the SP can train a single global or personalized model for each GS. Experiments show that the proposed models match or exceed a centrally trained model's performance and generalize well. Privacy is analyzed by assessing information leakage between data and shared activations of the GS model split. Additionally, differential privacy enhances local data privacy while examining its impact on performance. A transformer model is used as our base learner.
Code (1)
Tasks
energy managementLoad ForecastingPrivacy PreservingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Vulnerability Due to Training Order in Split Learning
Split learning (SL) is a privacy-preserving distributed deep learning method used to train a collaborative model without the need for sharing of patient's raw data between clients. In split learning, an additional privac…
Privacy PreservingBreaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting
Federated Learning (FL) enables multiple devices to collaboratively train a shared model while preserving data privacy. Ever-increasing model complexity coupled with limited memory resources on the participating devices …
Federated LearningCan We Use Split Learning on 1D CNN Models for Privacy Preserving Training?
A new collaborative learning, called split learning, was recently introduced, aiming to protect user data privacy without revealing raw input data to a server. It collaboratively runs a deep neural network model where th…
Privacy PreservingSplit Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is ch…
Computational EfficiencyDeep LearningFederated LearningPrivacy PreservingSecurity Analysis of SplitFed Learning
Split Learning (SL) and Federated Learning (FL) are two prominent distributed collaborative learning techniques that maintain data privacy by allowing clients to never share their private data with other clients and serv…
Federated LearningModel Poisoning