Private and rateless adaptive coded matrix-vector multiplication
Abstract Edge computing is emerging as a new paradigm to allow processing data near the edge of the network, where the data is typically generated and collected. This enables critical computations at the edge in applications such as Internet of Things (IoT), in which an increasing number of devices...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-01-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13638-020-01887-y |
| _version_ | 1818723819280400384 |
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| author | Rawad Bitar Yuxuan Xing Yasaman Keshtkarjahromi Venkat Dasari Salim El Rouayheb Hulya Seferoglu |
| author_facet | Rawad Bitar Yuxuan Xing Yasaman Keshtkarjahromi Venkat Dasari Salim El Rouayheb Hulya Seferoglu |
| author_sort | Rawad Bitar |
| collection | DOAJ |
| description | Abstract Edge computing is emerging as a new paradigm to allow processing data near the edge of the network, where the data is typically generated and collected. This enables critical computations at the edge in applications such as Internet of Things (IoT), in which an increasing number of devices (sensors, cameras, health monitoring devices, etc.) collect data that needs to be processed through computationally intensive algorithms with stringent reliability, security and latency constraints. Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. Coded computation is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a private and rateless adaptive coded computation (PRAC) algorithm for distributed matrix-vector multiplication by taking into account (1) the privacy requirements of IoT applications and devices, and (2) the heterogeneous and time-varying resources of edge devices. We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. We provide theoretical guarantees on the performance of PRAC and its comparison to baselines. Moreover, we confirm our theoretical results through simulations and implementations on Android-based smartphones. |
| first_indexed | 2024-12-17T21:16:35Z |
| format | Article |
| id | doaj.art-c8dbb55b9637476ca2b08c9ed93d1144 |
| institution | Directory Open Access Journal |
| issn | 1687-1499 |
| language | English |
| last_indexed | 2024-12-17T21:16:35Z |
| publishDate | 2021-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj.art-c8dbb55b9637476ca2b08c9ed93d11442022-12-21T21:32:20ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992021-01-012021112510.1186/s13638-020-01887-yPrivate and rateless adaptive coded matrix-vector multiplicationRawad Bitar0Yuxuan Xing1Yasaman Keshtkarjahromi2Venkat Dasari3Salim El Rouayheb4Hulya Seferoglu5Department of Electrical and Computer Engineering, Technical University of MunichDepartment of Electrical and Computer Engineering, University of Illinois at ChicagoStorage Research Group at the Seagate TechnologyUS Army Research LabDepartment of Electrical and Computer Engineering, Rutgers UniversityDepartment of Electrical and Computer Engineering, University of Illinois at ChicagoAbstract Edge computing is emerging as a new paradigm to allow processing data near the edge of the network, where the data is typically generated and collected. This enables critical computations at the edge in applications such as Internet of Things (IoT), in which an increasing number of devices (sensors, cameras, health monitoring devices, etc.) collect data that needs to be processed through computationally intensive algorithms with stringent reliability, security and latency constraints. Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. Coded computation is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a private and rateless adaptive coded computation (PRAC) algorithm for distributed matrix-vector multiplication by taking into account (1) the privacy requirements of IoT applications and devices, and (2) the heterogeneous and time-varying resources of edge devices. We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. We provide theoretical guarantees on the performance of PRAC and its comparison to baselines. Moreover, we confirm our theoretical results through simulations and implementations on Android-based smartphones.https://doi.org/10.1186/s13638-020-01887-yDistributed coded computingSecret sharingRateless private codesHeterogeneous computing clusters |
| spellingShingle | Rawad Bitar Yuxuan Xing Yasaman Keshtkarjahromi Venkat Dasari Salim El Rouayheb Hulya Seferoglu Private and rateless adaptive coded matrix-vector multiplication EURASIP Journal on Wireless Communications and Networking Distributed coded computing Secret sharing Rateless private codes Heterogeneous computing clusters |
| title | Private and rateless adaptive coded matrix-vector multiplication |
| title_full | Private and rateless adaptive coded matrix-vector multiplication |
| title_fullStr | Private and rateless adaptive coded matrix-vector multiplication |
| title_full_unstemmed | Private and rateless adaptive coded matrix-vector multiplication |
| title_short | Private and rateless adaptive coded matrix-vector multiplication |
| title_sort | private and rateless adaptive coded matrix vector multiplication |
| topic | Distributed coded computing Secret sharing Rateless private codes Heterogeneous computing clusters |
| url | https://doi.org/10.1186/s13638-020-01887-y |
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