Efficient and flexible management for industrial Internet of Things: A federated learning approach
In this paper, we devise an efficient and flexible management for mobile edge computing (MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the considered IIoT networks, all devices have some computational tasks to be computed with the help of some computational...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Guo, Yinghao [verfasserIn] Zhao, Zichao [verfasserIn] He, Ke [verfasserIn] Lai, Shiwei [verfasserIn] Xia, Junjuan [verfasserIn] Fan, Lisheng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Computer networks - Amsterdam [u.a.] : Elsevier, 1976, 192 |
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| Übergeordnetes Werk: |
volume:192 |
| DOI / URN: |
10.1016/j.comnet.2021.108122 |
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| Katalog-ID: |
ELV005988675 |
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| 245 | 1 | 0 | |a Efficient and flexible management for industrial Internet of Things: A federated learning approach |
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| 520 | |a In this paper, we devise an efficient and flexible management for mobile edge computing (MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the considered IIoT networks, all devices have some computational tasks to be computed with the help of some computational access points (CAPs). Although the performance of the IIoT networks can be optimized by using the resource allocation based on some centralized schemes, such solution is neither efficient nor flexible. To address this issue, we use a deep reinforcement learning (DRL) algorithm based federated learning algorithm to adjust three parameters: the task offloading ratio, bandwidth allocation ratio and transmit power. The optimization can minimize the normalized system cost, while reduce the communication cost in the optimization process. Moreover, simulation results are demonstrated to verify that the proposed federated framework can achieve an efficient and flexible management for the IIoT networks. | ||
| 650 | 4 | |a IIoT | |
| 650 | 4 | |a Efficient and flexible management | |
| 650 | 4 | |a Federated learning | |
| 650 | 4 | |a Mobile edge computing | |
| 700 | 1 | |a Zhao, Zichao |e verfasserin |4 aut | |
| 700 | 1 | |a He, Ke |e verfasserin |4 aut | |
| 700 | 1 | |a Lai, Shiwei |e verfasserin |4 aut | |
| 700 | 1 | |a Xia, Junjuan |e verfasserin |4 aut | |
| 700 | 1 | |a Fan, Lisheng |e verfasserin |4 aut | |
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10.1016/j.comnet.2021.108122 doi (DE-627)ELV005988675 (ELSEVIER)S1389-1286(21)00196-1 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Guo, Yinghao verfasserin (orcid)0000-0002-7799-6585 aut Efficient and flexible management for industrial Internet of Things: A federated learning approach 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we devise an efficient and flexible management for mobile edge computing (MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the considered IIoT networks, all devices have some computational tasks to be computed with the help of some computational access points (CAPs). Although the performance of the IIoT networks can be optimized by using the resource allocation based on some centralized schemes, such solution is neither efficient nor flexible. To address this issue, we use a deep reinforcement learning (DRL) algorithm based federated learning algorithm to adjust three parameters: the task offloading ratio, bandwidth allocation ratio and transmit power. The optimization can minimize the normalized system cost, while reduce the communication cost in the optimization process. Moreover, simulation results are demonstrated to verify that the proposed federated framework can achieve an efficient and flexible management for the IIoT networks. IIoT Efficient and flexible management Federated learning Mobile edge computing Zhao, Zichao verfasserin aut He, Ke verfasserin aut Lai, Shiwei verfasserin aut Xia, Junjuan verfasserin aut Fan, Lisheng verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 192 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:192 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 192 |
| spelling |
10.1016/j.comnet.2021.108122 doi (DE-627)ELV005988675 (ELSEVIER)S1389-1286(21)00196-1 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Guo, Yinghao verfasserin (orcid)0000-0002-7799-6585 aut Efficient and flexible management for industrial Internet of Things: A federated learning approach 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we devise an efficient and flexible management for mobile edge computing (MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the considered IIoT networks, all devices have some computational tasks to be computed with the help of some computational access points (CAPs). Although the performance of the IIoT networks can be optimized by using the resource allocation based on some centralized schemes, such solution is neither efficient nor flexible. To address this issue, we use a deep reinforcement learning (DRL) algorithm based federated learning algorithm to adjust three parameters: the task offloading ratio, bandwidth allocation ratio and transmit power. The optimization can minimize the normalized system cost, while reduce the communication cost in the optimization process. Moreover, simulation results are demonstrated to verify that the proposed federated framework can achieve an efficient and flexible management for the IIoT networks. IIoT Efficient and flexible management Federated learning Mobile edge computing Zhao, Zichao verfasserin aut He, Ke verfasserin aut Lai, Shiwei verfasserin aut Xia, Junjuan verfasserin aut Fan, Lisheng verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 192 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:192 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 192 |
| allfields_unstemmed |
10.1016/j.comnet.2021.108122 doi (DE-627)ELV005988675 (ELSEVIER)S1389-1286(21)00196-1 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Guo, Yinghao verfasserin (orcid)0000-0002-7799-6585 aut Efficient and flexible management for industrial Internet of Things: A federated learning approach 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we devise an efficient and flexible management for mobile edge computing (MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the considered IIoT networks, all devices have some computational tasks to be computed with the help of some computational access points (CAPs). Although the performance of the IIoT networks can be optimized by using the resource allocation based on some centralized schemes, such solution is neither efficient nor flexible. To address this issue, we use a deep reinforcement learning (DRL) algorithm based federated learning algorithm to adjust three parameters: the task offloading ratio, bandwidth allocation ratio and transmit power. The optimization can minimize the normalized system cost, while reduce the communication cost in the optimization process. Moreover, simulation results are demonstrated to verify that the proposed federated framework can achieve an efficient and flexible management for the IIoT networks. IIoT Efficient and flexible management Federated learning Mobile edge computing Zhao, Zichao verfasserin aut He, Ke verfasserin aut Lai, Shiwei verfasserin aut Xia, Junjuan verfasserin aut Fan, Lisheng verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 192 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:192 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 192 |
| allfieldsGer |
10.1016/j.comnet.2021.108122 doi (DE-627)ELV005988675 (ELSEVIER)S1389-1286(21)00196-1 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Guo, Yinghao verfasserin (orcid)0000-0002-7799-6585 aut Efficient and flexible management for industrial Internet of Things: A federated learning approach 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we devise an efficient and flexible management for mobile edge computing (MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the considered IIoT networks, all devices have some computational tasks to be computed with the help of some computational access points (CAPs). Although the performance of the IIoT networks can be optimized by using the resource allocation based on some centralized schemes, such solution is neither efficient nor flexible. To address this issue, we use a deep reinforcement learning (DRL) algorithm based federated learning algorithm to adjust three parameters: the task offloading ratio, bandwidth allocation ratio and transmit power. The optimization can minimize the normalized system cost, while reduce the communication cost in the optimization process. Moreover, simulation results are demonstrated to verify that the proposed federated framework can achieve an efficient and flexible management for the IIoT networks. IIoT Efficient and flexible management Federated learning Mobile edge computing Zhao, Zichao verfasserin aut He, Ke verfasserin aut Lai, Shiwei verfasserin aut Xia, Junjuan verfasserin aut Fan, Lisheng verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 192 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:192 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 192 |
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10.1016/j.comnet.2021.108122 doi (DE-627)ELV005988675 (ELSEVIER)S1389-1286(21)00196-1 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Guo, Yinghao verfasserin (orcid)0000-0002-7799-6585 aut Efficient and flexible management for industrial Internet of Things: A federated learning approach 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we devise an efficient and flexible management for mobile edge computing (MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the considered IIoT networks, all devices have some computational tasks to be computed with the help of some computational access points (CAPs). Although the performance of the IIoT networks can be optimized by using the resource allocation based on some centralized schemes, such solution is neither efficient nor flexible. To address this issue, we use a deep reinforcement learning (DRL) algorithm based federated learning algorithm to adjust three parameters: the task offloading ratio, bandwidth allocation ratio and transmit power. The optimization can minimize the normalized system cost, while reduce the communication cost in the optimization process. Moreover, simulation results are demonstrated to verify that the proposed federated framework can achieve an efficient and flexible management for the IIoT networks. IIoT Efficient and flexible management Federated learning Mobile edge computing Zhao, Zichao verfasserin aut He, Ke verfasserin aut Lai, Shiwei verfasserin aut Xia, Junjuan verfasserin aut Fan, Lisheng verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 192 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:192 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 192 |
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efficient and flexible management for industrial internet of things: a federated learning approach |
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Efficient and flexible management for industrial Internet of Things: A federated learning approach |
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In this paper, we devise an efficient and flexible management for mobile edge computing (MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the considered IIoT networks, all devices have some computational tasks to be computed with the help of some computational access points (CAPs). Although the performance of the IIoT networks can be optimized by using the resource allocation based on some centralized schemes, such solution is neither efficient nor flexible. To address this issue, we use a deep reinforcement learning (DRL) algorithm based federated learning algorithm to adjust three parameters: the task offloading ratio, bandwidth allocation ratio and transmit power. The optimization can minimize the normalized system cost, while reduce the communication cost in the optimization process. Moreover, simulation results are demonstrated to verify that the proposed federated framework can achieve an efficient and flexible management for the IIoT networks. |
| abstractGer |
In this paper, we devise an efficient and flexible management for mobile edge computing (MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the considered IIoT networks, all devices have some computational tasks to be computed with the help of some computational access points (CAPs). Although the performance of the IIoT networks can be optimized by using the resource allocation based on some centralized schemes, such solution is neither efficient nor flexible. To address this issue, we use a deep reinforcement learning (DRL) algorithm based federated learning algorithm to adjust three parameters: the task offloading ratio, bandwidth allocation ratio and transmit power. The optimization can minimize the normalized system cost, while reduce the communication cost in the optimization process. Moreover, simulation results are demonstrated to verify that the proposed federated framework can achieve an efficient and flexible management for the IIoT networks. |
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In this paper, we devise an efficient and flexible management for mobile edge computing (MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the considered IIoT networks, all devices have some computational tasks to be computed with the help of some computational access points (CAPs). Although the performance of the IIoT networks can be optimized by using the resource allocation based on some centralized schemes, such solution is neither efficient nor flexible. To address this issue, we use a deep reinforcement learning (DRL) algorithm based federated learning algorithm to adjust three parameters: the task offloading ratio, bandwidth allocation ratio and transmit power. The optimization can minimize the normalized system cost, while reduce the communication cost in the optimization process. Moreover, simulation results are demonstrated to verify that the proposed federated framework can achieve an efficient and flexible management for the IIoT networks. |
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