Middleware for Smart Campus applications based in Federated Learning
ResumoFederated Learning is a collaborative and distributed approach to creating Machine Learning models using data from several devices in an Internet of Things (IoT) network, maintaining anonymity and privacy, with potential to reduce the computational overhead to build these models and cope with the low communication capacity of these networks. In a Federated Learning system, a centralized service (aggregator) generates an arbitrary global model and sends it to the distributed components (workers), which train this model locally with their data. Then the workers send their local models to the aggregator, which unifies the local models into a new global model, in a process that can be repeated as often as necessary. In this context, this article proposes a middleware to simplify the development and deployment of Federated Learning models to IoT applications, focusing on Smart Campus scenarios. Using the abstraction provided by the middleware, the applications can easily authenticate as a new node, use the available models, and collaborate on models creation or evolution, without worrying about specific implementation details regarding the communication between the components, and the use of Machine Learning algorithms and frameworks. As a case study to validate the middleware concept and its initial implementation, an application for forecasting energy consumption is described, using an open dataset from different scenarios as input. In the evaluation described in this article, the Federated Learning model allowed a 60% reduction in the number of iterations to outperform a Long Short-Time Memory model trained by a standard Machine Learning system, with a R2 score of 0.98.
Mohammed Ali Al-Garadi, Amr Mohamed, Abdulla Khalid Al-Ali, Xiaojiang Du, Ihsan Ali, and Mohsen Guizani. 2020. A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Communications Surveys & Tutorials 22, 3 (2020), 1646–1685. https://doi.org/10.1109/COMST.2020.2988293
Carla Barcelos, João Gluz, and Rosa Vicari. 2011. An agent-based federated learning object search service. Interdisciplinary journal of e-learning and learning objects 7, 1 (2011), 37–54.
Monik Raj Behera, Sudhir Upadhyay, Robert Otter, and Suresh Shetty. 2020. Federated Learning using Distributed Messaging with Entitlements for Anonymous Computation and Secure Delivery of Model. (2020).
Mehdi Bennis, Mérouane Debbah, and H Vincent Poor. 2018. Ultrareliable and low-latency wireless communication: Tail, risk, and scale. Proc. IEEE 106, 10 (2018), 1834–1853. https://doi.org/10.1109/JPROC.2018.2867029
Bouziane Brik, Adlen Ksentini, and Maha Bouaziz. 2020. Federated learning for UAVs-enabled wireless networks: Use cases, challenges, and open problems. IEEE Access 8 (2020), 53841–53849. https://doi.org/10.1109/ACCESS.2020.2981430
Georgios Damaskinos, Rachid Guerraoui, Anne-Marie Kermarrec, Vlad Nitu, Rhicheek Patra, and Francois Taiani. 2020. Fleet: Online federated learning via staleness awareness and performance prediction. In Proceedings of the 21st International Middleware Conference. 163–177. https://doi.org/10.1145/3423211.3425685
Sai Venketesh Dasari, Kaushal Mittal, GVK Sasirekha, Jyotsna Bapat, and Debabrata Das. 2021. Privacy Enhanced Energy Prediction in Smart Building using Federated Learning. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). IEEE, 1–6. https://doi.org/10.1109/IEMTRONICS52119.2021.9422544
DAYTON 2022. DP&L Inc. https://www.aes-ohio.com/
DOM 2022. Dominion Energy, Inc. https://www.dominionenergy.com/
Song Guo, Deze Zeng, and Shifu Dong. 2020. Pedagogical data analysis via federated learning toward Education 4.0. American Journal of Education and Information Technology 4, 2 (2020), 56.
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, et al. 2019. Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019). https://doi.org/10.1561/2200000083 arXiv:1912.04977
Can Kaymakci, Lukas Baur, and Alexander Sauer. 2021. Federated Machine Learning Architecture for Energy-Efficient Industrial Applications. ESSN: 2701-6277 (2021). https://doi.org/10.15488/11237
Latif U Khan, Walid Saad, Zhu Han, Ekram Hossain, and Choong Seon Hong. 2021. Federated learning for internet of things: Recent advances, taxonomy, and open challenges. IEEE Communications Surveys & Tutorials (2021). https://doi.org/10.1109/COMST.2021.3090430
Jakub Konečny, H Brendan McMahan, Daniel Ramage, and Peter Richtárik. 2016. Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527 (2016). https://doi.org/10.48550/arXiv.1610.02527
Jakub Konečny, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. 2016. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016). https://doi.org/10.48550/arXiv.1610.05492
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37, 3 (2020), 50–60. https://doi.org/10.1109/MSP.2020.2975749
Angan Mitra, Yanik Ngoko, and Denis Trystram. 2021. Impact of Federated Learning On Smart Buildings. In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE, 93–99. https://doi.org/10.1109/ICAIS50930.2021.9395938
Elena Mocanu, Phuong H Nguyen, Madeleine Gibescu, and Wil L Kling. 2016. Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks 6 (2016), 91–99. https://doi.org/10.1016/j.segan.2016.02.005
Rob Mulla. 2018. Hourly Energy Consumption - Over 10 years of hourly energy consumption data from PJM in Megawatts. https://www.kaggle.com/datasets/robikscube/hourly-energy-consumption.
Dinh C Nguyen, Ming Ding, Pubudu N Pathirana, Aruna Seneviratne, Jun Li, and H Vincent Poor. 2021. Federated Learning for Internet of Things: A Comprehensive Survey. arXiv preprint arXiv:2104.07914 (2021).  PJM 2022. About PJM. https://doi.org/10.1109/COMST.2021.3075439 https://www.pjm.com/about-pjm.
I Gethzi Ahila Poornima and B Paramasivan. 2020. Anomaly detection in wireless sensor network using machine learning algorithm. Computer Communications 151 (2020), 331–337. https://doi.org/10.1016/j.comcom.2020.01.005
RNP. 2021. RNP anuncia projetos selecionados em seu Programa de P&D para 2022. [link].
Raed Abdel Sater and A Ben Hamza. 2021. A federated learning approach to anomaly detection in smart buildings. ACM Transactions on Internet of Things 2, 4 (2021), 1–23. https://doi.org/10.1145/3467981
Hichem Sedjelmaci, Sidi Mohammed Senouci, and Mohamad Al-Bahri. 2016. A lightweight anomaly detection technique for low-resource IoT devices: A game-theoretic methodology. In 2016 IEEE international conference on communications (ICC). IEEE, 1–6. https://doi.org/10.1109/ICC.2016.7510811
Yunchuan Shi, Wei Li, Xiaomin Chang, and Albert Y Zomaya. 2021. User privacy leakages from federated learning in NILM applications. In Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. 212–213. https://doi.org/10.1145/3486611.3492222
Wei Sun. 2022. Predictive Analysis and Simulation of College Sports Performance Fused with Adaptive Federated Deep Learning Algorithm. Journal of Sensors 2022 (2022). https://doi.org/10.1155/2022/1205622
Afaf Taïk and Soumaya Cherkaoui. 2020. Electrical load forecasting using edge computing and federated learning. In ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE, 1–6. https://doi.org/10.1109/ICC40277.2020.9148937
Lu Tan and Neng Wang. 2010. Future internet: The internet of things. In 2010 3rd international conference on advanced computer theory and engineering (ICACTE), Vol. 5. IEEE, V5–376. https://doi.org/10.1109/ICACTE.2010.5579543
Hui Yie Teh, Andreas W Kempa-Liehr, I Kevin, and Kai Wang. 2020. Sensor data quality: a systematic review. Journal of Big Data 7, 1 (2020), 1–49. https://doi.org/10.1186/s40537-020-0285-1
Vijay K Vemuri. 2020. The Hundred-Page Machine Learning Book: by Andriy Burkov, Quebec City, Canada, 2019, 160 pp., 49.99(Hardcover); 29.00 (paperback); 25.43(KindleEdition),(Alternatively,canpurchaseatleanpub.comataminimumpriceof 20.00), ISBN 978-1999579517. https://doi.org/10.1080/15228053.2020.1766224
Joost Verbraeken, Martijn de Vos, and Johan Pouwelse. 2021. Bristle: Decentralized Federated Learning in Byzantine, Non-iid Environments. arXiv preprint arXiv:2110.11006 (2021). https://doi.org/10.48550/arXiv.2110.11006
Hui Wu, Haiting Han, Xiao Wang, and Shengli Sun. 2020. Research on Artificial Intelligence Enhancing Internet of Things Security: A Survey. Ieee Access 8 (2020), 153826–153848. https://doi.org/10.1109/ACCESS.2020.3018170
Andrea Zanella, Nicola Bui, Angelo Castellani, Lorenzo Vangelista, and Michele Zorzi. 2014. Internet of things for smart cities. IEEE Internet of Things journal 1, 1 (2014), 22–32. https://doi.org/10.1109/JIOT.2014.2306328
Shijie Zhang, Zhezhuang Xu, Jinlong Wang, Jian Chen, and Yuxiong Xia. 2021. Improving the Accuracy of Load Forecasting for Campus Buildings Based on Federated Learning. In 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC), Vol. 1. IEEE, 1–5. https://doi.org/10.1109/ICNSC52481.2021.9702205