Hypothesis Transfer Learning for Efficient Data Computing in Smart Cities Environments
Open accessAbstract
It is commonly assumed that in a smart city there will be thousands of mostly mobile/wireless smart devices (e.g. sensors, smart-phones, etc.) that will continuously generate big amounts of data. Data will have to be collected and processed in order to extract knowledge out of it, to feed users' and smart city applications. A typical approach to process such big amounts of data is to i) gather all the collected data on the cloud through wireless pervasive networks, and ii) perform data analysis operations exploiting machine learning techniques. However, according to many studies, this centralised cloud-based approach may not be sustainable from a networking point of view. The joint effect of data-intensive users' multimedia applications and smart cities monitoring and control applications may result in severe network congestions making applications hardly usable. To cope with this problem, in this paper we propose a distributed machine learning approach that does not require to move data in a centralised cloud platform, but processes it directly where it is collected. Specifically, we exploit Hypothesis Transfer Learning (HTL) to build a distributed machine learning framework. In our framework we train a series of partial models, each ''residing'' in a location where a subset of the dataset is generated. We then refine the partial models by exchanging them between locations, thus obtaining a unique complete model. Using an activity classification task on a reference dataset as a concrete example, we show that the classification accuracy of the HTL model is comparable with that of a model built out of the complete dataset, but the cost in term of network overhead is dramatically reduced. We then perform a sensitiveness analysis to characterise how the overhead depends on key parameters. It is also worth noticing that the HTL approach is suitable for applications dealing with privacy sensitive data, as data can stay where they are generated, and do not need to be transferred to third parties, i.e., to a cloud provider, to extract knowledge out of it.
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ML is key to preprocessing large amounts of data in large sensor networks, allowing only relevant data to be transmitted instead of all raw data.
“ML is key to preprocessing large amounts of data in large sensor networks, allowing only what is relevant to be transmitted, instead of all the raw data that is being collected [480,672,804].”
From Tackling climate change with machine learning (2023) · cites this paper as [804]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Habibzadeh, H., Boggio-Dandry, A., Qin, Z., Soyata, T., Kantarci, B., & Mouftah, H. T. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700304 · Cited by 64 · see citing works
- Authors
- Valerio, L., Passarella, A., & Conti, M.
- Affiliations
- Institute of Informatics and Telematics (IT)
- Venue
- 2016 IEEE International Conference on Smart Computing (SMARTCOMP)
- Publisher
- IEEE
- Published
- 2016-05
- Volume
- 6
- Pages
- 1-8
- OpenAlex
- W2475667482
- Cited by
- 24
- References
- 17
- Citations by year
- 2023: 3 · 2022: 5 · 2021: 2 · 2020: 2 · 2019: 2 · 2018: 3 · 2017: 6 · 2016: 1
- FWCI
- 3.42
- Type
- conference paper
- Language
- en
- Open access
- green
- puma.isti.cnr.it
- Licence
- other-oa
- Locations
- IEEE International Conference on Smart Computing (publishedVersion)
- Funders
- Horizon 2020 Framework Programme
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- distributed learning, smart cities, edge computing, network congestion, activity classification, network overhead, privacy-sensitive data, wireless sensor networks, big data processing
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