Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach

Published: 30 November 2022 Publication History

Abstract

It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment.

References

[1]
Nguyen DC, Ding M, Pham Q-V, Long L, Seneviratne A, Pathirana P, Li J, Niyato D, and Poor HV Federated learning meets blockchain in edge computing: opportunities and challenges IEEE Internet Things J. 2021 8 12806-12825
[2]
Li Z et al. RR-LADP: a privacy-enhanced federated learning scheme for internet of everything IEEE Consumer Electron. Magazine 2021 10 5 93-101
[3]
Nguyen DC, Cheng P, Ding M, Lopez-Perez D, Pathirana PN, Li J, Seneviratne A, Li Y, and Poor HV Enabling AI in future wireless networks: a data life cycle perspective IEEE Commun. Surv. Tutor. 2020 23 1 553-595
[4]
Wang S, Tuor T, Salonidis T, Leung KK, Makaya C, He T, and Chan K Adaptive federated learning in resource-constrained edge computing systems IEEE J. Sel. Areas Commun. 2019 37 6 1205-1221
[5]
Ma C, Li J, Ding M, Yang HH, Shu F, Quek TQS, and Poor HV On safeguarding privacy and security in the framework of federated learning IEEE Netw. 2020 34 4 242-248
[6]
Nguyen DC, Pathirana PN, Ding M, and Seneviratne A BEdgeHealth: a decentralized architecture for edge-based IoMT networks using blockchain IEEE Internet Things J. 2021 8 12 11743-11757
[7]
Majeed, U., Hong, C.S.: FLchain: federated learning via MECenabled blockchain network. In: 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1–4 (2019)
[8]
Kim, Y.J., Hong, C.S., Blockchain-based node-aware dynamic weighting methods for improving federated learning performance. In: 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1–4 (2019)
[9]
Yang Q, Liu Y, Chen T, and Tong Y Federated machine learning: concept and applications ACM Trans. Intell. Syst. Technol. 2019 10 2 1-19
[10]
Park J, Samarakoon S, Elgabli A, Kim J, Bennis M, Kim S-L, and Debbah M Communication-efficient and distributed learning over wireless networks: principles and applications Proc. IEEE 2021 109 5 796-819
[11]
Niknam S, Dhillon HS, and Reed JH Federated learning for wireless communications: motivation, opportunities, and challenges IEEE Commun. Mag. 2020 58 6 46-51
[12]
Sharma K, Chen MY, and Park JH A software-defined fog node based distributed blockchain cloud architecture for IoT IEEE Access 2018 6 115-124
[13]
Pan J, Wang J, Hester A, Alqerm I, Liu Y, and Zhao Y EdgeChain: an edge-IoT framework and prototype based on blockchain and smart contracts IEEE Internet Things J. 2019 6 3 4719-4732
[14]
Rahman MA, Rashid MM, Hossain MS, Hassanain E, Alhamid MF, and Guizani M Blockchain and IoT-based cognitive edge framework for sharing economy services in a smart city IEEE Access 2019 7 18611-18621
[15]
Xu, A. et al.: Efficiency and security for edge computing assisted smart grids. In: 2019 IEEE Globecom Workshops (GC Wkshps), pp. 1–5 (2019)
[16]
Wu Y, Dai H-N, and Wang H Convergence of blockchain and edge computing for secure and scalable IIoT critical infrastructures in Industry 4.0 IEEE Internet Things J. 2021 8 4 2300-2317
[17]
Pajooh, H.H. et al.: Hyperledger fabric blockchain for securing the edge Internet of Things. Sensors (Basel, Switzerland) 21(2), Article 359 (2021).
[18]
Ren Y, Leng Y, Cheng Y, and Wanf J Secure data storage based on blockchain and coding in edge computing J. Math. Biosci. Eng. 2019 16 4 1874-1892
[19]
Kuo, T.T., Ohno-Machado, L.: Model chain: decentralized privacy-preserving healthcare predictive modeling framework on private blockchain networks. arXiv preprint (2018). arXiv:1802.01746
[20]
Rathore, S., Park, J.H.: DeepBlockIoTNet: a secure deep learning approach with blockchain for the IoT network. Trans. Ind. Inform. 11(14), Article 3974 (2019)
[21]
Ferrag MA and Maglaras L DeepCoin: a novel deep learning and blockchain-based energy exchange framework for smart grids IEEE Trans. Eng. Manage. 2019 67 4 1285-1297
[22]
Singh S, Jeong Y, and Park J A deep learning-based IoT oriented infrastructure for secure smart City Sustain. Cities Soc. 2020 60
[23]
He Y, Wang Y, Qiu C, Lin Q, Li J, and Ming Z Blockchain-based edge computing resource allocation in IoT: a deep reinforcement learning approach IEEE Internet Things J. 2021 8 4 2226-2237
[24]
Dai H-N, Wu Y, Wang H, Imran M, and Haider N Blockchain-empowered edge intelligence for Internet of Medical Things against COVID-19 IEEE Internet Things Mag 2021 4 2 34-39
[25]
Li D et al. Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey Soft Comput. 2022 26 4423-4440
[26]
Nguyen DC et al. Federated learning meets blockchain in edge computing: opportunities and challenges IEEE Internet Things J. 2021 8 16 12806-12825
[27]
Lu Y, Huang X, Zhang K, Maharjan S, and Zhang Y Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles IEEE Trans. Veh. Technol. 2020 69 4 4298-4311
[28]
Brauer F, Castillo-Chavez C, and Feng Z Mathematical Models in Epidemiology 2019 New York Springer
[29]
Giordano G et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy Nat. Med. 2020 26 6 855-860
[30]
Lim WYB et al. Federated learning in mobile edge networks: a comprehensive survey IEEE Commun. Surv. Tutor. 2020 22 3 2031-2063
[31]
Zhao Z, Feng C, Yang HH, and Luo X Federated-learning enabled intelligent fog radio access networks: fundamental theory, key techniques, and future trends IEEE Wirel. Commun. 2020 27 2 22-28
[32]
Briggs, C., Fan, Z., Andras, P.: A review of privacy-preserving federated learning for private IoT analytics. arXiv Preprint (2020). arXiv:2004.11794
[33]
Yang R, Yu FR, Si P, Yang Z, and Zhang Y Integrated blockchain and edge computing systems: a survey, some research issues, and challenges IEEE Commun. Surv. Tutor. 2019 21 2 1508-1532
[34]
Queralta, J.P., Westerlund, T.: blockchain for mobile edge computing: consensus mechanisms and scalability. arXiv Preprint (2020). arXiv:2006.07578
[35]
Li T, Sahu AK, Talwalkar A, and Smith V Federated learning: challenges, methods, and future directions IEEE Signal Process. Mag. 2020 37 3 50-60
[36]
Du Z, Wu C, Yoshinaga T, Yau K-LA, Ji Y, and Li J Federated learning for vehicular Internet of Things: recent advances and open issues IEEE Open J. Comput. Soc. 2020 1 45-61
[37]
Samarakoon S, Bennis M, Saad W, and Debbah M Distributed federated learning for ultra-reliable low-latency vehicular communications IEEE Trans. Commun. 2020 68 2 1146-1159
[38]
Tran, N.H., Bao, W., Zomaya, A., Nguyen, M.N.H., Hong, C.S.: Federated learning over wireless networks: optimization model design and analysis. In: Proceedings of the IEEE Conference on Computer Communications (INFOCOM), pp. 1387–1395 (2019)
[39]
Nguyen DC, Pathirana PN, Ding M, and Seneviratne A Blockchain for 5G and beyond networks: a state of the art survey J. Netw. Comput. Appl. 2020 166
[40]
Fang, M., Cao, X., Jia, J., Gong, N.: Local Model poisoning attacks to byzantine-robust federated learning. In: 29th {USENIX} Security Symposium ({USENIX} Security 20), pp. 1605–1622 (2020)
[41]
Mothukuri V, Parizi RM, Pouriyeh S, Huang Y, Dehghantanha A, and Srivastava G A survey on security and privacy of federated learning Futur. Gener. Comput. Syst. 2021 115 619-640
[42]
Wei K, Li J, Ding M, Ma C, Yang HH, Farokhi F, Jin S, Quek TQS, and Poor HV Federated learning with differential privacy: algorithms and performance analysis IEEE Trans. Inf. Forensics Secur. 2020 15 3454-3469
[43]
Nguyen, D.C., Pathirana, P.N., Ding, M., Seneviratne, A.: Blockchain and edge computing for decentralized E.M.R.s sharing in federated healthcare. In: 2020 IEEE Global Communications Conference, Taipei, Taiwan, pp. 1–6 (2020)
[44]
Kim H, Park J, Bennis M, and Kim S-L Blockchained on-device federated learning IEEE Commun. Lett. 2020 24 6 1279-1283
[45]
Pokhrel SR and Choi J Federated learning with blockchain for autonomous vehicles: analysis and design challenges IEEE Trans. Commun. 2020 68 8 4734-4746
[46]
Lu Y, Huang X, Zhang K, Maharjan S, and Zhang Y Low-latency federated learning and blockchain for edge association in digital twin empowered 6G networks IEEE Trans. Ind. Inf. 2020 17 7 5098-5107
[47]
Lu Y, Huang X, Zhang K, Maharjan S, et al. Blockchain empowered asynchronous federated learning for secure data sharing in the Internet of vehicles IEEE Trans. Veh. Technol. 2020 69 4 4298-4311
[48]
Sattler F, Wiedemann S, Muller K-R, and Samek W Robust and communication-efficient federated learning from non-i.i.d. data IEEE Trans. Neural Netw. Learn. Syst. 2019 31 9 3400-3413
[49]
Nguyen DC, Pathirana PN, Ding M, and Seneviratne A Integration of blockchain and cloud of things: architecture, applications, and challenges IEEE Commun. Surv. Tutor. 2020 22 4 2521-2549
[50]
Kim H, Kim S-H, Hwang JY, and Seo C Efficient privacy preserving machine learning for blockchain network IEEE Access 2019 7 136481-136495
[51]
Liaskos, S., Wang, B., Alimohammadi, N.: Blockchain networks as adaptive systems. In: IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), Montreal, QC, Canada, May 2019, pp. 139–145 (2019)
[52]
Wang, L., Wang, W., Li, B.: CMFL: mitigating communication overhead for federated learning. In: Proceedings of the IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 954–964 (2019)
[53]
Mills J, Hu J, and Min G Communication-efficient federated learning for wireless edge intelligence in IoT IEEE Internet Things J. 2020 7 7 5986-5994
[54]
Yang F, Zhou W, Wu Q, Long R, Xiong NN, and Zhou M Delegated proof of stake with downgrade: a secure and efficient blockchain consensus algorithm with downgrade mechanism IEEE Access 2019 7 118541-118555
[55]
Liu Y, Wang K, Lin Y, and Xu W LightChain: a lightweight blockchain system for industrial Internet of Things IEEE Trans. Ind. Inf. 2019 15 6 3571-3581
[56]
Hieu, N.Q., Anh, T.T., Luong, N.C., Niyato, D., Kim, D.I., Elmroth, E.: Resource management for blockchain-enabled federated learning: a deep reinforcement learning approach, pp. 1–5. arXiv preprint (2020). arXiv:2004.04104
[57]
Nguyen HT, Hoang DT, Luong NC, Niyato D, and Kim DI A hierarchical game model for OFDM integrated radar and communication systems IEEE Trans. Veh. Technol. 2021 70 5 5077-5082
[58]
Lu Y, Huang X, Zhang K, Maharjan S, and Zhang Y Communication-efficient federated learning for digital twin edge networks in industrial IoT IEEE Trans. Ind. Inf. 2020
[59]
Liao Z and Couillet R A large dimensional analysis of least squares support vector machines IEEE Trans. Signal Process. 2019 67 4 1065-1074
[60]
Liu M, Yu FR, Teng Y, Leung VCM, and Song M Distributed resource allocation in blockchain-based video streaming systems with mobile edge computing IEEE Trans. Wirel. Commun. 2019 18 1 695-708
[61]
Zhan Y, Li P, Qu Z, Zeng D, and Guo S A learning-based incentive mechanism for federated learning IEEE Internet Things J. 2020 7 7 6360-6368
[62]
Toyoda, K., Zhang, A.N.: Mechanism design for an incentive-aware blockchain-enabled federated learning platform. In: Proceedings of the IEEE International Conference on Big Data (Big Data), pp. 395–403 (2019)
[63]
Ur Rehman, M.H., Salah, K., Damiani, E., Svetinovic, D.: Towards blockchain-based reputation-aware federated learning. In: Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 183–188 (2020)
[64]
BaranwalSomy, N., Kannan, K., Arya, V., Hans, S., Singh, A., Lohia, P., Mehta, S.: Ownership preserving AI market places using blockchain. In: Proceedings of the 2019 IEEE International Conference on Blockchain (Blockchain), pp. 156–165 (2019)
[65]
Bao, X., Su, C., Xiong, Y., Huang, W., Hu, Y.: FLChain: a blockchain for auditable federated learning with trust and incentive. In: Proceedings of the 5th International Conference on Big Data Computing and Communications (BIGCOM), pp. 151–159 (2019)
[66]
Weng J, Weng J, Zhang J, Li M, Zhang Y, and Luo W DeepChain: auditable and privacy-preserving deep learning with blockchain-based incentive IEEE Trans. Depend. Secure Comput. 2021 18 5 2438-2455
[67]
Martinez, I., Francis, S., Hafid, A.S.: Record and reward federated learning contributions with blockchain. In: Proceedings of the International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), pp. 50–57 (2019)
[68]
Nguyen DC, Pathirana PN, Ding M, and Seneviratne A Blockchain for secure E.H.R.s sharing of mobile cloud-based E-health systems IEEE Access 2019 7 66792-66806
[69]
Jiao Y, Wang P, Niyato D, and Suankaewmanee K Auction mechanisms in cloud/fog computing resource allocation for public blockchain networks IEEE Trans. Parallel Distrib. Syst. 2019 30 9 1975-1989
[70]
Schmid, R., Pfitzner, B., Beilharz, J., Arnrich, B., Polze, A.: Tangle ledger for decentralized learning. IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 852–859 (2020)
[71]
Qu Y, Gao L, Luan TH, Xiang Y, Yu S, Li B, and Zheng G Decentralized privacy using blockchain-enabled federated learning in fog computing IEEE Internet Things J. 2020 7 6 5171-5183
[72]
Wang Q, Guo Y, Wang X, Ji T, Yu L, and Li P AI at the edge: blockchain-empowered secure multiparty learning with heterogeneous models IEEE Internet Things J. 2020 7 10 9600-9610
[73]
Arachchige PCM, Bertok P, Khalil I, Liu D, Camtepe S, and Atiquzzaman M A trustworthy privacy-preserving framework for machine learning in industrial IoT systems IEEE Trans. Ind. Inf. 2020 16 9 6092-6102
[74]
Zhao Y, Zhao J, Yang M, Wang T, Wang N, Lyu L, Niyato D, and Lam K-Y Local differential privacy based federated learning for Internet of Things IEEE Internet Things J. 2020 8 11 8836-8853
[75]
Ma, S., Cao, Y., Xiong, L.: Transparent contribution evaluation for secure federated learning on blockchain, pp. 1–4. arXiv preprint (2021) arXiv:2101.10572
[76]
Lugan S, Desbordes P, Brion E, Ramos-Tormo LX, Legay A, and Macq B Secure architectures implementing trusted coalitions for blockchained distributed learning (TCLearn) IEEE Access 2019 7 181789-181799
[77]
Li X, Wang Y, Song J, Liu Y, Zhang X, Zhou K, and Li C A low cost and un-canceled Laplace noise-based differential privacy algorithm for spatial decompositions World Wide Web 2020 23 1 549-572
[78]
Zhang S and Lee J-H Double-spending with a Sybil attack in the bitcoin decentralized network IEEE Trans. Ind. Inf. 2019 15 10 5715-5722
[79]
Chen Y, Qin X, Wang J, Yu C, and Gao W FedHealth: a federated transfer learning framework for wearable healthcare IEEE Intell. Syst. 2020 35 4 83-93
[80]
Wang Y, Su Z, Zhang N, and Benslimane A Learning in the air: secure federated learning for UAV-assisted crowd sensing IEEE Trans. Netw. Sci. Eng. 2020 8 2 1055-1069
[81]
Pham Q-V, Fang F, Ha VN, Piran MJ, Le M, Le LB, Hwang W-J, and Ding Z A survey of multi-access edge computing in 5g and beyond: fundamentals, technology integration, and state-of the-art IEEE Access 2020 8 116974-117017
[82]
Lu Y, Huang X, Dai Y, Maharjan S, and Zhang Y Blockchain and federated learning for privacy-preserved data sharing in industrial IoT IEEE Trans. Ind. Inf. 2020 16 6 4177-4186
[83]
Chai H, Leng S, Chen Y, and Zhang K A hierarchical blockchain enabled federated learning algorithm for knowledge sharing in the internet of vehicles IEEE Trans. Intell. Transp. Syst. 2020 22 7 3975-3986
[84]
Yin B, Yin H, Wu Y, and Jiang Z F.D.C.: A secure federated deep learning mechanism for data collaborations in the internet of things IEEE Internet Things J. 2020 7 7 6348-6359
[85]
Cui L, Su X, Ming Z, Chen Z, Yang S, Zhou Y, and Xiao W Creat: Blockchain-assisted compression algorithm of federated learning for content caching in edge computing IEEE Internet Things J. 2020 9 16 14151-14161
[86]
Garg N, Sellathurai M, Bhatia V, Bharath BN, and Ratnarajah T Online content popularity prediction and learning in wireless edge caching IEEE Trans. Commun. 2020 68 2 1087-1100
[87]
Wang X, Han Y, Wang C, Zhao Q, Chen X, and Chen M In-Edge AI: intelligentizing mobile edge computing, caching, and communication by federated learning IEEE Netw. 2019 33 5 156-165
[88]
Kulkarni, V., Kulkarni, M., Pant, A.: Survey of personalization techniques for federated learning. In: 4th World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), pp. 1–4 (2020)
[89]
Pandey SR, Tran NH, Bennis M, Tun YK, Manzoor A, and Hong CS A crowdsourcing framework for on-device federated learning IEEE Trans. Wirel. Commun. 2020 19 5 3241-3256
[90]
Zhao Y, Zhao J, Jiang L, Tan R, Niyato D, Li Z, Lyu L, and Liu Y Privacy-preserving blockchain-based federated learning for IoT devices IEEE Internet Things J. 2020 8 3 1817-1829
[91]
Li X, Jiang P, Chen T, Luo X, and Wen Q A survey on the security of blockchain systems Futur. Gener. Comput. Syst. 2020 107 C 841-853
[92]
Wang, S., Wang, C., Hu, Q.: Corking by forking: vulnerability analysis of blockchain. In: Proceedings of the IEEE Conference on Computer Communications, Paris, France, April 2019, pp. 829–837 (2019)
[93]
Zhang S and Lee J-H Mitigations on Sybil-based double-spend attacks in bitcoin IEEE Consum. Electron. Mag. 2021 10 5 23-28
[94]
Zhang, J., Zhang, J., Chen, J., Yu, S.: GAN enhanced membership inference: a passive local attack in federated learning. In: Proceedings of the 2020 IEEE International Conference on Communications (I.C.C.), Dublin, Ireland, pp. 1–6 (2020)
[95]
Silva, P.: Impact of geo-distribution and mining pools on blockchains: a study of ethereum—practical experience report and ongoing Ph.D. work. In: Proceedings of the 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S), pp. 73–74 (2020)
[96]
Shlezinger N, Chen M, Eldar YC, Poor HV, and Cui S UVeQFed: universal vector quantization for federated learning IEEE Trans. Signal Process. 2021 69 500-514
[97]
Jhunjhunwala, D., Gadhikar, A., Joshi, G., Eldar, Y.C.: Adaptive quantization of model updates for communication-efficient federated learning. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–10 (2021)
[98]
Chen Z, Tian P, Liao W, and Yu W Zero-knowledge clustering based adversarial mitigation in heterogeneous federated learning IEEE Trans. Netw. Sci. Eng. 2021 8 2 1070-1083
[99]
Rothchild, D., Panda, A., Ullah, E., Ivkin, N., Stoica, I., Braverman, V., Gonzalez, J., Arora, R.: FetchSGD: communication-efficient federated learning with sketching. In: International Conference on Machine Learning, November 20, pp. 8253–8265 (2020)
[100]
Ghosh, A., Hong, J., Yin, D., Ramchandran, K.: Robust federated learning in a heterogeneous environment. arXiv Preprint (2019). arXiv:1906.06629
[101]
Wang Y, Su Z, and Zhang N BSIS: blockchain-based secure incentive scheme for energy delivery in vehicular energy network IEEE Trans. Ind. Inf. 2019 15 6 3620-3631
[102]
Nishio, T., Yonetani, R.: Client selection for federated learning with heterogeneous resources in mobile edge. In: Proceedings of the IEEE International Conference on Communications (I.C.C.), May 19, pp. 1–7 (2019)
[103]
Tang C, Wu L, Wen G, and Zheng Z Incentivizing honest mining in blockchain networks: a reputation approach IEEE Trans. Circuits Syst. II Express Briefs 2020 67 1 117-121
[104]
Wang EK, Liang Z, Chen C-M, Kumari S, and Khan MK PoRX: a reputation incentive scheme for blockchain consensus of IIoT Futur. Gener. Comput. Syst. 2020 102 140-151
[105]
Chuan M, Jun L, Ming D, Long S, Taotao W, Zhu H, and Vincent PH When federated learning meets blockchain: a new distributed learning paradigm IEEE Comput. Intell. Mag. 2022 17 3 26-33
[106]
Diro A, Reda H, Chilamkurti N, Mahmood A, Zaman N, and Nam Y Lightweight authenticated-encryption scheme for Internet of Things based on publish-subscribe communication IEEE Access 2020 8 60539-60551
[107]
Shi, W., Zhou, S., Niu, Z.: Device scheduling with fast convergence for wireless federated learning. In: Proceedings of the IEEE International Conference on Communications (I.C.C.), pp. 1–6 (2020)
[108]
Biswas S, Sharif K, Li F, Maharjan S, Mohanty SP, and Wang Y PoBT: a lightweight consensus algorithm for scalable IoT business blockchain IEEE Internet Things J. 2020 7 3 2343-2355
[109]
Nguyen DC, Pathirana PN, Ding M, and Seneviratne A Privacy preserved task offloading in mobile blockchain with deep reinforcement learning IEEE Trans. Netw. Serv. Manage. 2020 17 4 2536-2549
[110]
Yang H, Xiong Z, Zhao J, Niyato D, Yuen C, and Deng R Deep reinforcement learning based massive access management for ultra-reliable low-latency communications IEEE Trans. Wirel. Commun. 2021 20 5 2977-2990

Cited By

View all
  • (2025)Federated learning incentivize with privacy-preserving for IoT in edge computing in the context of B5GCluster Computing10.1007/s10586-024-04788-728:2Online publication date: 1-Apr-2025

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Cluster Computing
Cluster Computing  Volume 27, Issue 1
Feb 2024
1123 pages

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 30 November 2022
Accepted: 05 November 2022
Revision received: 21 October 2022
Received: 16 July 2022

Author Tags

  1. Blockchain technology
  2. Edge computing
  3. Federated learning systems
  4. Machine learning techniques
  5. Voting classifier

Qualifiers

  • Research-article

Funding Sources

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 11 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)Federated learning incentivize with privacy-preserving for IoT in edge computing in the context of B5GCluster Computing10.1007/s10586-024-04788-728:2Online publication date: 1-Apr-2025

View Options

View options

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media