A Secure Federated Deep Learning-Based Approach for Heating Load Demand Forecasting in Building Environment

Moradzadeh, Arash; Moayyed, Hamed; Mohammadi‐Ivatloo, Behnam; Aguiar, A. Pedro; Anvari‐Moghaddam, Amjad

doi:10.1109/access.2021.3139529

Cited by 29 publications

(11 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, this approach efficiently reduces the poisoning attacks. Moradzadeh proposed a method for safe load forecasting based on Cyber-Secure Federated Deep Learning (CSFDL) [10]. The approach protects data security and resists cyber-attacks, improving prediction accuracy.…”

Section: Load Forecasting-oriented Secure Aggregation Methods Researc...mentioning

confidence: 99%

“…Substituting this value into the equation for the Gaussian distribution function, we obtain Equation (10).…”

Section: Aggregation Of the Global Model Based On Distancementioning

confidence: 99%

“…During the model training process, if the server aggregates the uploaded parameters without evaluating their reasonableness, it could affect the accuracy of the overall model parameters. Subsequently, other participants who use the centralized model parameters issued by the server for local model training could experience reduced model accuracy [10]. The current aggregation methods for load prediction models in federated learning have certain limitations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fed‐SAD: A secure aggregation federated learning method for distributed short‐term load forecasting

Li,

Wu,

Wang

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

The distributed and privacy‐preserving attributes of fine‐grained smart grid data create obstacles to data sharing. As a result, federated learning emerges as an effective strategy for collaborative training in distributed load forecasting. However, poisoning attacks can interfere with training in the federated learning aggregation process, making it challenging to ensure the accuracy and safety of the global model in load forecasting. Therefore, the authors propose a secure aggregation federated learning method based on similarity and distance (Fed‐SAD) for distributed load forecasting. The server determines approximate global model parameters based on the similarity of the model parameters of the participants and aggregates the global model parameters using a distance‐based weighting method. Fed‐SAD effectively reduces the interference of poisoning attacks by securely aggregating models in short‐term load forecasting. Experimental results show that using the Fed‐SAD results in mean absolute percentage error (MAPE) reduction for certain participants by 19% on sign flipping attacks, MAPE reduction by 15% on additive noise attacks, and MAPE reduction by 4.5% without attack, compared to the Federated Average Aggregation algorithm. Furthermore, Fed‐SAD consistently maintains robustness and has excellent attack detection accuracy.

show abstract

Section: Load Forecasting-oriented Secure Aggregation Methods Researc...mentioning

confidence: 99%

“…Substituting this value into the equation for the Gaussian distribution function, we obtain Equation (10).…”

Section: Aggregation Of the Global Model Based On Distancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fed‐SAD: A secure aggregation federated learning method for distributed short‐term load forecasting

Li,

Wu,

Wang

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…As shown in Fig. 3 , the implementation process of the developed model is possible in 4 steps as follows (Moradzadeh et al 2021a ): Step 1 (local training): All clients receive the parameters θ t of the global model from the central server for local training. Then, the clients’ exclusive local models are trained by employing their own data.…”

Section: Methodologiesmentioning

confidence: 99%

Generalized global solar radiation forecasting model via cyber-secure deep federated learning

Moradzadeh,

Moayyed,

Mohammadi-Ivatloo

et al. 2023

Environ Sci Pollut Res

Self Cite

View full text Add to dashboard Cite

Recently, the increasing prevalence of solar energy in power and energy systems around the world has dramatically increased the importance of accurately predicting solar irradiance. However, the lack of access to data in many regions and the privacy concerns that can arise when collecting and transmitting data from distributed points to a central server pose challenges to current predictive techniques. This study proposes a global solar radiation forecasting approach based on federated learning (FL) and convolutional neural network (CNN). In addition to maintaining input data privacy, the proposed procedure can also be used as a global supermodel. In this paper, data related to eight regions of Iran with different climatic features are considered as CNN input for network training in each client. To test the effectiveness of the global supermodel, data related to three new regions of Iran named Abadeh, Jarqavieh, and Arak are used. It can be seen that the global forecasting supermodel was able to forecast solar radiation for Abadeh, Jarqavieh, and Arak regions with 95%, 92%, and 90% accuracy coefficients, respectively. Finally, in a comparative scenario, various conventional machine learning and deep learning models are employed to forecast solar radiation in each of the study regions. The results of the above approaches are compared and evaluated with the results of the proposed FL-based method. The results show that, since no training data were available from regions of Abadeh, Jarqavieh, and Arak, the conventional methods were not able to forecast solar radiation in these regions. This evaluation confirms the high ability of the presented FL approach to make acceptable predictions while preserving privacy and eliminating model reliance on training data.

show abstract

“…When employing cloud-based machine learning tools, private information must be managed safely, discreetly, and morally to avoid serious privacy violations ( Dasari et al, 2021 ). In addition to rules, privacy-enhanced ML techniques, such as federated learning techniques, are crucial in mitigating ethical and privacy violations ( Moradzadeh et al, 2021 ; Cheng, Li & Liu, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Privacy preserved and decentralized thermal comfort prediction model for smart buildings using federated learning

Abbas,

Alsubai,

Sampedro

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

Thermal comfort is a crucial element of smart buildings that assists in improving, analyzing, and realizing intelligent structures. Energy consumption forecasts for such smart buildings are crucial owing to the intricate decision-making processes surrounding resource efficiency. Machine learning (ML) techniques are employed to estimate energy consumption. ML algorithms, however, require a large amount of data to be adequate. There may be privacy violations due to collecting this data. To tackle this problem, this study proposes a federated deep learning (FDL) architecture developed around a deep neural network (DNN) paradigm. The study employs the ASHRAE RP-884 standard dataset for experimentation and analysis, which is available to the general public. The data is normalized using the min-max normalization approach, and the Synthetic Minority Over-sampling Technique (SMOTE) is used to enhance the minority class’s interpretation. The DNN model is trained separately on the dataset after obtaining modifications from two clients. Each client assesses the data greatly to reduce the over-fitting impact. The test result demonstrates the efficiency of the proposed FDL by reaching 82.40% accuracy while securing the data.

show abstract

A Secure Federated Deep Learning-Based Approach for Heating Load Demand Forecasting in Building Environment

Abstract: Amjad Anvari-Moghaddam and Behnam Mohammadi-Ivatloo acknowledge the support of the "HeatReFlex-Green and Flexible Heating/Cooling" project (www.heatreflex.et.aau.

Cited by 29 publications

References 39 publications

Fed‐SAD: A secure aggregation federated learning method for distributed short‐term load forecasting

Fed‐SAD: A secure aggregation federated learning method for distributed short‐term load forecasting

Generalized global solar radiation forecasting model via cyber-secure deep federated learning

Privacy preserved and decentralized thermal comfort prediction model for smart buildings using federated learning

Contact Info

Product

Resources

About