FedNILM: Applying Federated Learning to NILM Applications at the Edge

Tang, Guoming; Huang, Qianyi; Wang, Yi; Wang, Xudong; Lou, Jiadong

doi:10.1109/tgcn.2022.3167392

Cited by 43 publications

(33 citation statements)

References 21 publications

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“…In our future work, we first plan to use a wide range of singleboard computers to replace the Dask Fargate cluster to act as the geographically distributed edge devices to implement our prototype system. This will provide us with a more sensitive testing environment to study the effectiveness of our framework compared to not just those GBDT-based approaches but also the other available alternatives [9,49,54]. Second, we will explore performance of node-level parallelism on the leaf-wise model.…”

Section: Discussionmentioning

confidence: 99%

Fed-GBM

Chang

Zomaya

2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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Non-intrusive load monitoring (NILM) is a computational technique to allow appliance-level energy disaggregation for sustainable energy management. Most NILM models require considerable training data to capture sufficient appliance signatures for robust model fitting. However, local on-site training cannot satisfy that requirement due to limited data availability. It is thus conceivable to perform data collaboration among different stakeholders. Unfortunately, current collaborative learning approaches rely on deep learning, encryption, and differential privacy techniques associated with either expensive computation or inefficient communication. In this paper, we propose a cost-effective collaborative learning framework, Fed-GBM (Federated Gradient Boosting Machines), consisting of two-stage voting and node-level parallelism, to address the problems in comodelling for NILM. Through extensive experiments on real-world residential datasets, Fed-GBM shows remarkable performance on convergence, accuracy, computation and communication efficiency. The impact of hyper-parameters in Fed-GBM is also extensively studied to guide better practical use. CCS CONCEPTS• Computing methodologies → Machine learning; • Hardware → Power and energy.

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Section: Discussionmentioning

confidence: 99%

Fed-GBM

Chang

Zomaya

2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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“…On the other hand, the emergence of federated learning [26] not only provides privacy guarantees for smart meter data but solves the challenge of data isolation, which therefore brings considerable benefits to DNN-based NILM models. Despite its promising future, applying FL to NILM has only received attention very recently [27,28,29,30]. For instance, [27] proposed a FederatedNILM framework to enable NILM task in the FL paradigm at the residential level.…”

Section: Nilm and Its Privacy-preserving Methodsmentioning

confidence: 99%

DP$^2$-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring

Dai¹,

Meng²,

Wang³

et al. 2022

Preprint

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Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumption, can help analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications. Recent studies have proposed many novel NILM frameworks based on federated deep learning (FL). However, there lacks comprehensive research exploring the utility optimization schemes and the privacy-preserving schemes in different FL-based NILM application scenarios. In this paper, we make the first attempt to conduct FL-based NILM focusing on both the utility optimization and the privacy-preserving by developing a distributed and privacy-preserving NILM (DP 2 -NILM) framework and carrying out comparative experiments on practical NILM scenarios based on real-world smart meter datasets. Specifically, two alternative federated learning strategies are examined in the utility optimization schemes, i.e., the FedAvg and the FedProx. Moreover, different levels of privacy guarantees, i.e., the local differential privacy federated learning and the global differential privacy federated learning are provided in the DP 2 -NILM. Extensive comparison experiments are conducted on three real-world datasets to evaluate the proposed framework.

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“…Also, TL models can use either supervised, semisupervised, or unsupervised learning. 8,117,119 Data scarcity can be a serious issue when applying smart NILM solutions, especially those based on DL models. The latter requires huge amounts of data to learn individual (unidentifiable) loads accurately.…”

Section: Learning Models (L)mentioning

confidence: 99%

“…TL : TL‐based NILM systems are part of the event‐based category as they use classification models after detecting appliance load events. Also, TL models can use either supervised, semisupervised, or unsupervised learning 8,117,119 . Data scarcity can be a serious issue when applying smart NILM solutions, especially those based on DL models.…”

Section: Overview Of Recent Smart Nilm Algorithmsmentioning

confidence: 99%

“…For example, edge model personalizations, edge resource restrictions, and edge training data scarcity. To that end, Zhang et al 119 introduce FedNILM, a practical FL framework for energy disaggregation at the edge client. It is developed to ensure security, preserve privacy, and deliver tailored NILM services to large‐scale edge clients.…”

Section: Overview Of Recent Smart Nilm Algorithmsmentioning

confidence: 99%

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Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Himeur

Alsalemi

Bensaali

et al. 2022

Int J of Intelligent Sys

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Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first Int J Intell Syst. 2022;37:7124-7179. wileyonlinelibrary.com/journal/int investigated, including data collection devices and public data sets. Next, event-based and non-eventbased NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

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FedNILM: Applying Federated Learning to NILM Applications at the Edge

Cited by 43 publications

References 21 publications

Fed-GBM

Fed-GBM

DP$^2$-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

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