Pub Date : 2023-04-13DOI: 10.48550/arXiv.2304.06551
Deng Pan, M. Khoshkholghi, Toktam Mahmoodi
Unmanned aerial vehicles (UAV) or drones play many roles in a modern smart city such as the delivery of goods, mapping real-time road traffic and monitoring pollution. The ability of drones to perform these functions often requires the support of machine learning technology. However, traditional machine learning models for drones encounter data privacy problems, communication costs and energy limitations. Federated Learning, an emerging distributed machine learning approach, is an excellent solution to address these issues. Federated learning (FL) allows drones to train local models without transmitting raw data. However, existing FL requires a central server to aggregate the trained model parameters of the UAV. A failure of the central server can significantly impact the overall training. In this paper, we propose two aggregation methods: Commutative FL and Alternate FL, based on the existing architecture of decentralised Federated Learning for UAV Networks (DFL-UN) by adding a unique aggregation method of decentralised FL. Those two methods can effectively control energy consumption and communication cost by controlling the number of local training epochs, local communication, and global communication. The simulation results of the proposed training methods are also presented to verify the feasibility and efficiency of the architecture compared with two benchmark methods (e.g. standard machine learning training and standard single aggregation server training). The simulation results show that the proposed methods outperform the benchmark methods in terms of operational stability, energy consumption and communication cost.
{"title":"Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networks","authors":"Deng Pan, M. Khoshkholghi, Toktam Mahmoodi","doi":"10.48550/arXiv.2304.06551","DOIUrl":"https://doi.org/10.48550/arXiv.2304.06551","url":null,"abstract":"Unmanned aerial vehicles (UAV) or drones play many roles in a modern smart city such as the delivery of goods, mapping real-time road traffic and monitoring pollution. The ability of drones to perform these functions often requires the support of machine learning technology. However, traditional machine learning models for drones encounter data privacy problems, communication costs and energy limitations. Federated Learning, an emerging distributed machine learning approach, is an excellent solution to address these issues. Federated learning (FL) allows drones to train local models without transmitting raw data. However, existing FL requires a central server to aggregate the trained model parameters of the UAV. A failure of the central server can significantly impact the overall training. In this paper, we propose two aggregation methods: Commutative FL and Alternate FL, based on the existing architecture of decentralised Federated Learning for UAV Networks (DFL-UN) by adding a unique aggregation method of decentralised FL. Those two methods can effectively control energy consumption and communication cost by controlling the number of local training epochs, local communication, and global communication. The simulation results of the proposed training methods are also presented to verify the feasibility and efficiency of the architecture compared with two benchmark methods (e.g. standard machine learning training and standard single aggregation server training). The simulation results show that the proposed methods outperform the benchmark methods in terms of operational stability, energy consumption and communication cost.","PeriodicalId":186438,"journal":{"name":"Mobile Computing, Applications, and Services","volume":"86 26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131045949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-04DOI: 10.1007/978-3-030-99203-3_5
Hugues Moreau, A. Vassilev, Liming Chen
{"title":"When Neural Networks Using Different Sensors Create Similar Features","authors":"Hugues Moreau, A. Vassilev, Liming Chen","doi":"10.1007/978-3-030-99203-3_5","DOIUrl":"https://doi.org/10.1007/978-3-030-99203-3_5","url":null,"abstract":"","PeriodicalId":186438,"journal":{"name":"Mobile Computing, Applications, and Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115681272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-12DOI: 10.1007/978-3-030-64214-3_3
Hu Song, Qianjun Wu, Yuhang Chen, Meiya Dong, Rong-Yuan Wang
{"title":"Design of a Security Service Orchestration Framework for NFV","authors":"Hu Song, Qianjun Wu, Yuhang Chen, Meiya Dong, Rong-Yuan Wang","doi":"10.1007/978-3-030-64214-3_3","DOIUrl":"https://doi.org/10.1007/978-3-030-64214-3_3","url":null,"abstract":"","PeriodicalId":186438,"journal":{"name":"Mobile Computing, Applications, and Services","volume":" 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133021060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-14DOI: 10.1007/978-3-030-28468-8_11
M. Cavojský, M. Drozda
{"title":"Comparison of User Trajectories with the Needleman-Wunsch Algorithm","authors":"M. Cavojský, M. Drozda","doi":"10.1007/978-3-030-28468-8_11","DOIUrl":"https://doi.org/10.1007/978-3-030-28468-8_11","url":null,"abstract":"","PeriodicalId":186438,"journal":{"name":"Mobile Computing, Applications, and Services","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114666539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-14DOI: 10.1007/978-3-030-28468-8_10
Gerald Henderson, Qi Han
{"title":"Distributed Learning Automata Based Data Dissemination in Networked Robotic Systems","authors":"Gerald Henderson, Qi Han","doi":"10.1007/978-3-030-28468-8_10","DOIUrl":"https://doi.org/10.1007/978-3-030-28468-8_10","url":null,"abstract":"","PeriodicalId":186438,"journal":{"name":"Mobile Computing, Applications, and Services","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116733610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-14DOI: 10.1007/978-3-030-28468-8_7
Shenglong Yan, Juan Yu, Houpan Zhou
{"title":"Fast Map-Matching Based on Hidden Markov Model","authors":"Shenglong Yan, Juan Yu, Houpan Zhou","doi":"10.1007/978-3-030-28468-8_7","DOIUrl":"https://doi.org/10.1007/978-3-030-28468-8_7","url":null,"abstract":"","PeriodicalId":186438,"journal":{"name":"Mobile Computing, Applications, and Services","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121607981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-14DOI: 10.1007/978-3-030-28468-8_15
Shuo Wang, Qianmu Li, Shunmei Meng, Bo Zhang, Cangqi Zhou
{"title":"An Active Defense Model in Edge Computing Based on Network Topology Mimetic Correlation","authors":"Shuo Wang, Qianmu Li, Shunmei Meng, Bo Zhang, Cangqi Zhou","doi":"10.1007/978-3-030-28468-8_15","DOIUrl":"https://doi.org/10.1007/978-3-030-28468-8_15","url":null,"abstract":"","PeriodicalId":186438,"journal":{"name":"Mobile Computing, Applications, and Services","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126055442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-14DOI: 10.1007/978-3-030-28468-8_6
Shamar Akio Ward, Mechelle Gittens
{"title":"Edu-BUS Wi-Fi: An On-Board Wi-Fi Educational System Using a Raspberry Pi","authors":"Shamar Akio Ward, Mechelle Gittens","doi":"10.1007/978-3-030-28468-8_6","DOIUrl":"https://doi.org/10.1007/978-3-030-28468-8_6","url":null,"abstract":"","PeriodicalId":186438,"journal":{"name":"Mobile Computing, Applications, and Services","volume":"173 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132683887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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