Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Po-Leen Ooi, Melanie; Sohail, Shaleeza; Huang, Victoria; Hudson, Nathaniel; Baughman, Matt; Rana, Omer; Hinze, Annika; Chard, Kyle; Chard, Ryan; Foster, Ian

doi:10.1109/MIM.2023.10328671

Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Journal Article · Fri Dec 01 04:00:00 EST 2023 · IEEE Instrumentation and Measurement Magazine

DOI:https://doi.org/10.1109/MIM.2023.10328671· OSTI ID:2280851

Po-Leen Ooi, Melanie; Sohail, Shaleeza; Huang, Victoria; Hudson, Nathaniel; Baughman, Matt; Rana, Omer; Hinze, Annika; Chard, Kyle; Chard, Ryan; Foster, Ian

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

Research Organization:: Argonne National Laboratory (ANL)

Sponsoring Organization:: U.S. Department of Energy (Office not specified); Royal Society of New Zealand

DOE Contract Number:: AC02-06CH11357

OSTI ID:: 2280851

Journal Information:: IEEE Instrumentation and Measurement Magazine, Journal Name: IEEE Instrumentation and Measurement Magazine Journal Issue: 9 Vol. 26; ISSN 1094-6969

Country of Publication:: United States

Language:: English

References (29)

Towards a Framework for the Levels and Aspects of Self-aware Computing Systems Lewis, Peter; Bellman, Kirstie L.; Landauer, Christopher Self-Aware Computing Systems https://doi.org/10.1007/978-3-319-47474-8_3	book	January 2017
A Distributed Game Theoretic Approach for Blockchain-based Offloading Strategy Liu, Weikang; Cao, Bin; Zhang, Lei ICC 2020 - 2020 IEEE International Conference on Communications (ICC) https://doi.org/10.1109/ICC40277.2020.9149311	conference	June 2020
Parsl: Pervasive Parallel Programming in Python Babuji, Yadu; Foster, Ian; Wilde, Michael Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing - HPDC '19 https://doi.org/10.1145/3307681.3325400	conference	January 2019
A Systematic Literature Review on Federated Machine Learning Lo, Sin Kit; Lu, Qinghua; Wang, Chen ACM Computing Surveys, Vol. 54, Issue 5 https://doi.org/10.1145/3450288	journal	May 2021
Service Management for Enabling Self-Awareness in Low-Power IoT Edge Devices Attarha, Shadi; Forster, Anna 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) https://doi.org/10.1109/PerComWorkshops53856.2022.9767362	conference	March 2022
Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks Tursunboev, Jamshid; Kang, Yong-Sung; Huh, Sung-Bum Applied Sciences, Vol. 12, Issue 2 https://doi.org/10.3390/app12020670	journal	January 2022
Dynamic and adaptive fault-tolerant asynchronous federated learning using volunteer edge devices Morell, José Ángel; Alba, Enrique Future Generation Computer Systems, Vol. 133 https://doi.org/10.1016/j.future.2022.02.024	journal	August 2022
Edge-Computing Architectures for Internet of Things Applications: A Survey Hamdan, Salam; Ayyash, Moussa; Almajali, Sufyan Sensors, Vol. 20, Issue 22 https://doi.org/10.3390/s20226441	journal	November 2020
Internet of Medical Things and Edge Computing for Improving Healthcare in Smart Cities Alrazgan, Muna Mathematical Problems in Engineering, Vol. 2022 https://doi.org/10.1155/2022/5776954	journal	March 2022
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling Luo, Bing; Xiao, Wenli; Wang, Shiqiang IEEE INFOCOM 2022 - IEEE Conference on Computer Communications https://doi.org/10.1109/INFOCOM48880.2022.9796935	conference	May 2022
A Performance Evaluation of Federated Learning Algorithms Nilsson, Adrian; Smith, Simon; Ulm, Gregor Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning https://doi.org/10.1145/3286490.3286559	conference	December 2018
Advances and Open Problems in Federated Learning Kairouz, Peter; McMahan, H. Brendan; Avent, Brendan Foundations and Trends® in Machine Learning, Vol. 14, Issue 1–2 https://doi.org/10.1561/2200000083	journal	January 2021
Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence Hao, Meng; Li, Hongwei; Luo, Xizhao IEEE Transactions on Industrial Informatics, Vol. 16, Issue 10 https://doi.org/10.1109/TII.2019.2945367	journal	October 2020
Federated learning with adaptive communication compression under dynamic bandwidth and unreliable networks Zhang, Xiongtao; Zhu, Xiaomin; Wang, Ji Information Sciences, Vol. 540 https://doi.org/10.1016/j.ins.2020.05.137	journal	November 2020
Federated learning and differential privacy for medical image analysis Adnan, Mohammed; Kalra, Shivam; Cresswell, Jesse C. Scientific Reports, Vol. 12, Issue 1 https://doi.org/10.1038/s41598-022-05539-7	journal	February 2022
Practical Secure Aggregation for Privacy-Preserving Machine Learning Bonawitz, Keith; Ivanov, Vladimir; Kreuter, Ben CCS '17: 2017 ACM SIGSAC Conference on Computer and Communications Security, Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security https://doi.org/10.1145/3133956.3133982	conference	October 2017
Personalised Federated Learning with BERT Fine Tuning. Case Study on Twitter Sentiment Analysis Ruiz-Millán, Jose Antonio; Martínez-Cámara, Eugenio; Victoria Luzón, M. Lecture Notes in Networks and Systems https://doi.org/10.1007/978-3-030-85365-5_19	book	January 2022
A Learning-Based Incentive Mechanism for Federated Learning Zhan, Yufeng; Li, Peng; Qu, Zhihao IEEE Internet of Things Journal, Vol. 7, Issue 7 https://doi.org/10.1109/JIOT.2020.2967772	journal	July 2020
Resource optimizing federated learning for use with IoT: A systematic review da Silva, Leylane Graziele Ferreira; Sadok, Djamel F. H.; Endo, Patricia Takako Journal of Parallel and Distributed Computing, Vol. 175 https://doi.org/10.1016/j.jpdc.2023.01.006	journal	May 2023
Edge computing: A survey Khan, Wazir Zada; Ahmed, Ejaz; Hakak, Saqib Future Generation Computer Systems, Vol. 97 https://doi.org/10.1016/j.future.2019.02.050	journal	August 2019
funcX: A Federated Function Serving Fabric for Science Chard, Ryan; Babuji, Yadu; Li, Zhuozhao HPDC '20: The 29th International Symposium on High-Performance Parallel and Distributed Computing, Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing https://doi.org/10.1145/3369583.3392683	conference	June 2020
Privacy-Preserving Federated Learning Using Homomorphic Encryption Park, Jaehyoung; Lim, Hyuk Applied Sciences, Vol. 12, Issue 2 https://doi.org/10.3390/app12020734	journal	January 2022
Record and Reward Federated Learning Contributions with Blockchain Martinez, Ismael; Francis, Sreya; Hafid, Abdelhakim Senhaji 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) https://doi.org/10.1109/CyberC.2019.00018	conference	October 2019
FeARH: Federated machine learning with anonymous random hybridization on electronic medical records Cui, Jianfei; Zhu, He; Deng, Hao Journal of Biomedical Informatics, Vol. 117 https://doi.org/10.1016/j.jbi.2021.103735	journal	May 2021
A differential privacy-based classification system for edge computing in IoT Xue, Wanli; Shen, Yiran; Luo, Chengwen Computer Communications, Vol. 182 https://doi.org/10.1016/j.comcom.2021.10.038	journal	January 2022
Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records Huang, Li; Shea, Andrew L.; Qian, Huining Journal of Biomedical Informatics, Vol. 99 https://doi.org/10.1016/j.jbi.2019.103291	journal	November 2019
Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions Zhang, Wei; Li, Xiang Structural Health Monitoring, Vol. 21, Issue 4 https://doi.org/10.1177/14759217211029201	journal	July 2021
Optimized Multi-Service Tasks Offloading for Federated Learning in Edge Virtualization Tam, Prohim; Math, Sa; Kim, Seokhoon IEEE Transactions on Network Science and Engineering, Vol. 9, Issue 6 https://doi.org/10.1109/TNSE.2022.3200057	journal	November 2022
Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT Yang, Wei; Xiang, Wei; Yang, Yuan IEEE Transactions on Industrial Informatics, Vol. 19, Issue 2 https://doi.org/10.1109/TII.2022.3183465	journal	February 2023

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