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Intersection of machine learning and mobile crowdsourcing: a systematic topic-driven review

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Abstract

During the past decade of the big data era, mobile crowdsourcing has emerged as a popular research area, leveraging the collective intelligence and engagement of a vast number of individuals using their mobile devices. Another actively evolving area is machine learning, which has recently been augmented by the mobile crowdsourcing approach, especially for data collection and labeling. However, what happens when these two prevailing concepts meet? What topics have been discussed in recent literature? This paper adopts a systematic methodology, leveraging Latent Dirichlet allocation topic modeling for topic discovery from recent publications, to provide a comprehensive and insightful review of the intersection of machine learning and mobile crowdsourcing. Moreover, the paper highlights the emerging federated learning technology that integrates elements from both concepts. Key research questions are answered by examining discovered topics. The paper thoroughly discusses state-of-the-art developments and trends in combining these two concepts and explains the role of one concept in the other. The paper also addresses remaining challenges and outlines a future research agenda, including the potential incorporation of large language models into mobile crowdsourcing systems.

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Data availability

The articles reviewed in this paper can be found in mainstream databases such as EI Compendex and Scopus by Elsevier. The list of reviewed articles and relevant analysis data can be made available by the authors upon request.

References

  1. Birhane A, Kasirzadeh A, Leslie D, Wachter S (2023) Science in the age of large language models. Nat Rev Phys 5(5):277–280. https://doi.org/10.1038/s42254-023-00581-4

    Article  Google Scholar 

  2. Vaughan JW (2017) Making better use of the crowd: how crowdsourcing can advance machine learning research. J Mach Learn Res 18(1):7026–7071

    MathSciNet  Google Scholar 

  3. Ray A, Chowdhury C, Bhattacharya S, Roy S (2023) A survey of mobile crowdsensing and crowdsourcing strategies for smart mobile device users. CCF Trans Pervasive Comput Interact 5(1):98–123. https://doi.org/10.1007/s42486-022-00110-9

    Article  Google Scholar 

  4. Guo M-H et al (2022) Attention mechanisms in computer vision: A survey. Comput Vis Media 8(3):331–368. https://doi.org/10.1007/s41095-022-0271-y

    Article  Google Scholar 

  5. Sun T-X, Liu X-Y, Qiu X-P, Huang X-J (2022) Paradigm shift in natural language processing. Mach Intell Res 19(3):169–183. https://doi.org/10.1007/s11633-022-1331-6

    Article  Google Scholar 

  6. Chen W, El Majzoub A, Al-Qudah I, Rabhi FA (2023) A CEP-driven framework for real-time news impact prediction on financial markets. Serv Orient Comput Appl. https://doi.org/10.1007/s11761-023-00358-8

    Article  Google Scholar 

  7. Chen W, Hussain W, Cauteruccio F, Zhang X (2024) Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models. Comput Model Eng Sci 139(1):187–224. [Online]. Available: http://www.techscience.com/CMES/v139n1/55114

  8. Zhang A, Xing L, Zou J, Wu JC (2022) Shifting machine learning for healthcare from development to deployment and from models to data. Nat Biomed Eng 6(12):1330–1345. https://doi.org/10.1038/s41551-022-00898-y

    Article  Google Scholar 

  9. Chen W, Rabhi F, Liao W, Al-Qudah I (2023) Leveraging State-of-the-art topic modeling for news impact analysis on financial markets: a comparative study. Electronics 12(12):2605. [Online]. Available: https://www.mdpi.com/2079-9292/12/12/2605

  10. Wohlin C (2014) Guidelines for snowballing in systematic literature studies and a replication in software engineering," presented at the Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, London, England, United Kingdom. [Online]. Available: https://doi.org/10.1145/2601248.2601268

  11. Capponi A, Fiandrino C, Kantarci B, Foschini L, Kliazovich D, Bouvry P (2019) A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities. IEEE Commun Surv Tutor 21(3):2419–2465. https://doi.org/10.1109/COMST.2019.2914030

    Article  Google Scholar 

  12. Kim JW, Edemacu K, Jang B (2022) Privacy-preserving mechanisms for location privacy in mobile crowdsensing: A survey. J Netw Comput Appl 200:103315. https://doi.org/10.1016/j.jnca.2021.103315

    Article  Google Scholar 

  13. Phuttharak J, Loke SW (2019) A review of mobile crowdsourcing architectures and challenges: toward crowd-empowered internet-of-Things. IEEE Access 7:304–324. https://doi.org/10.1109/ACCESS.2018.2885353

    Article  Google Scholar 

  14. Yao Z et al (2023) Machine learning for a sustainable energy future. Nat Rev Mater 8(3):202–215. https://doi.org/10.1038/s41578-022-00490-5

    Article  Google Scholar 

  15. Greener JG, Kandathil SM, Moffat L, Jones DT (2022) A guide to machine learning for biologists. Nat Rev Mol Cell Biol 23(1):40–55. https://doi.org/10.1038/s41580-021-00407-0

    Article  Google Scholar 

  16. Sodagari S (2022) Trends for mobile IoT crowdsourcing privacy and security in the big data era. IEEE Trans Technol Soc 3(3):199–225. https://doi.org/10.1109/TTS.2022.3191515

    Article  Google Scholar 

  17. Alghasham M, Alzakan M, Al-Hagery M (2023) A review of trending crowdsourcing topics in software engineering highlighting mobile crowdsourcing and AI utilization. Int J Adv Comput Sci Appl 14(4)

  18. Dimililer K, Dindar H, Al-Turjman F (2021) Deep learning, machine learning and internet of things in geophysical engineering applications: An overview. Microprocess Microsyst 80 https://doi.org/10.1016/j.micpro.2020.103613

  19. Karakaya A-S, Ritter T, Biessmann F, Bermbach D (2023) CycleSense: Detecting near miss incidents in bicycle traffic from mobile motion sensors. Pervasive Mobile Comput 91. https://doi.org/10.1016/j.pmcj.2023.101779

  20. Jaeger KL, Sando R, Dunn SB, Gendaszek AS (2023) Predicting probabilities of late summer surface flow presence in a glaciated mountainous headwater region. Hydrol Process 37(2). https://doi.org/10.1002/hyp.14813

  21. Saha J, Roy S, Das TK, Purkait K, Chowdhury C (2022) Designing data validation framework for crowd-sourced road monitoring applications. J Inst Eng (India) Ser B 103(4):1083–1096. https://doi.org/10.1007/s40031-022-00713-x

    Article  Google Scholar 

  22. Ibnatta Y, Khaldoun M, Sadik M (2022) Indoor localization system based on mobile access point model MAPM using RSS With UWB-OFDM. IEEE Access 10:46043–46056. https://doi.org/10.1109/ACCESS.2022.3168677

    Article  Google Scholar 

  23. Al-Shaery AM et al (2022) Real-time pilgrims management using wearable physiological sensors, mobile technology and artificial intelligence. IEEE Access 10:120891–120900. https://doi.org/10.1109/ACCESS.2022.3221771

    Article  Google Scholar 

  24. Mairittha N, Mairittha T, Lago P, Inoue S (2021) CrowdAct: achieving high-qality crowdsourced datasets in mobile activity recognition. Proc ACM Interact Mobile Wearable Ubiquit Technol 51. https://doi.org/10.1145/3432222

  25. Pereira R et al (2021) GreenHub: a large-scale collaborative dataset to battery consumption analysis of android devices. Empir Softw Eng 26(3). https://doi.org/10.1007/s10664-020-09925-5

  26. Shahid E, Arain QA (2021) Indoor positioning: “an image-based crowdsource machine learning approach.” Multimedia Tools Appl 80(17):26213–26235. https://doi.org/10.1007/s11042-021-10906-z

    Article  Google Scholar 

  27. Pandey SR, Tran NH, Bennis M, Tun YK, Manzoor A, Hong CS (2020) A crowdsourcing framework for on-device federated learning. IEEE Trans Wireless Commun 19(5):3241–3256. https://doi.org/10.1109/TWC.2020.2971981

    Article  Google Scholar 

  28. Wu C et al (2020) An automated machine-learning approach for road pothole detection using smartphone sensor data. Sensors (Switzerland) 20(19):1–23. https://doi.org/10.3390/s20195564

    Article  Google Scholar 

  29. Zhang J, Pan J, Cai Z, Li M, Cui L (2020) Knowledge transfer using user-generated data within real-time cloud services. KSII Trans Internet Inf Syst 14(1):77–92. https://doi.org/10.3837/tiis.2020.01.005

    Article  Google Scholar 

  30. Wang S et al (2020) Mapping crop types in southeast india with smartphone crowdsourcing and deep learning. Remote Sens 12(18). https://doi.org/10.3390/RS12182957

  31. Li W, Zhang C, Tanaka Y (2020) Pseudo label-driven federated learning-based decentralized indoor localization via mobile crowdsourcing. IEEE Sens J 20(19):11556–11565. https://doi.org/10.1109/JSEN.2020.2998116

    Article  Google Scholar 

  32. Tang H, Xiao M, Gao G, Zhao H (2020) Reverse-auction-based crowdsourced labeling for active learning. World Wide Web 23(1):671–689. https://doi.org/10.1007/s11280-019-00744-3

    Article  Google Scholar 

  33. Zhao Y, He T, Chen Z (2019) A unified framework for bug report assignment. Int J Software Eng Knowl Eng 29(4):607–628. https://doi.org/10.1142/S0218194019500256

    Article  Google Scholar 

  34. Tang S, Qin X, Wei G (2018) Network-based video quality assessment for encrypted HTTP adaptive streaming. IEEE Access 6:56246–56257. https://doi.org/10.1109/ACCESS.2018.2872932

    Article  Google Scholar 

  35. Nayebi M, Cho H, Ruhe G (2018) App store mining is not enough for app improvement. Empir Softw Eng 23(5):2764–2794. https://doi.org/10.1007/s10664-018-9601-1

    Article  Google Scholar 

  36. Latif S, Qadir J, Farooq S, Imran MA (2017) How 5G wireless (and Concomitant Technologies) will revolutionize healthcare?. Future Internet 9(4). https://doi.org/10.3390/fi9040093

  37. Fox A, Kumar BVKV, Chen J, Bai F (2017) Multi-lane pothole detection from crowdsourced undersampled vehicle sensor data. IEEE Trans Mob Comput 16(12):3417–3430. https://doi.org/10.1109/TMC.2017.2690995

    Article  Google Scholar 

  38. El Fazziki A, Benslimane D, Sadiq A, Ouarzazi J, Sadgal M (2017) An agent based traffic regulation system for the roadside air quality control. IEEE Access 5:13192–13201. https://doi.org/10.1109/ACCESS.2017.2725984

    Article  Google Scholar 

  39. Chi M, Sun Z, Qin Y, Shen J, Benediktsson JA (2017) A novel methodology to label urban remote sensing images based on location-based social media photos. Proc IEEE 105(10):1926–1936. https://doi.org/10.1109/JPROC.2017.2730585

    Article  Google Scholar 

  40. Vrysis L, Tsipas N, Dimoulas C, Papanikolaou G (2016) Crowdsourcing audio semantics by means of hybrid bimodal segmentation with hierarchical classification. AES J Audio Eng Soc 64(12):1042–1054. https://doi.org/10.17743/jaes.2016.0051

    Article  Google Scholar 

  41. Zeng W, Huang X, Muller Arisona S, McLoughlin IV (2014) Classifying watermelon ripeness by analysing acoustic signals using mobile devices. Personal Ubiquit Comput 18(7):1753–1762. https://doi.org/10.1007/s00779-013-0706-7

    Article  Google Scholar 

  42. Aly M, Rahouma KH, Ramzy SM (2022) Pay attention to the speech: COVID-19 diagnosis using machine learning and crowdsourced respiratory and speech recordings. Alex Eng J 61(5):3487–3500. https://doi.org/10.1016/j.aej.2021.08.070

    Article  Google Scholar 

  43. Straub T, Nagy M, Sidorov M, Tonetto L, Frey M, Gauterin F (2020) Energetic Map Data Imputation: A Machine Learning Approach. Energies 13(4):982. [Online]. Available: https://www.mdpi.com/1996-1073/13/4/982.

  44. Vlachogiannis DM, Moura S, Macfarlane J (2023) Intersense: An XGBoost model for traffic regulator identification at intersections through crowdsourced GPS data. Transp Res Part C Emerg Technol 151:104112. https://doi.org/10.1016/j.trc.2023.104112

    Article  Google Scholar 

  45. Overko R, Ordóñez-Hurtado R, Zhuk S, Ferraro P, Cullen A, Shorten R (2022) Spatial Positioning Token (SPToken) for Smart Mobility. IEEE Trans Intell Transp Syst 23(2):1529–1542. https://doi.org/10.1109/TITS.2020.3029537

    Article  Google Scholar 

  46. Torres-Sospedra J, Gaibor DPQ, Nurmi J, Koucheryavy Y, Lohan ES, Huerta J (2023) Scalable and efficient clustering for fingerprint-based positioning. IEEE Internet Things J 10(4):3484–3499. https://doi.org/10.1109/JIOT.2022.3230913

    Article  Google Scholar 

  47. Yin Y, Grundstein A, Mishra DR, Ramaswamy L, HashemiTonekaboni N, Dowd J (2021) DTEx: A dynamic urban thermal exposure index based on human mobility patterns. Environ Int 155:106573. https://doi.org/10.1016/j.envint.2021.106573

    Article  Google Scholar 

  48. Alsaleh N, Farooq B (2021) Interpretable data-driven demand modelling for on-demand transit services. Transp Res Part A Policy Pract 154:1–22. https://doi.org/10.1016/j.tra.2021.10.001

    Article  Google Scholar 

  49. Abououf M, Singh S, Otrok H, Mizouni R, Damiani E (2022) Machine Learning in Mobile Crowd Sourcing: A Behavior-Based Recruitment Model. ACM Trans Internet Technol 22(1). https://doi.org/10.1145/3451163

  50. Wang W et al (2022) Privacy protection federated learning system based on blockchain and edge computing in mobile crowdsourcing. Comput Netw 215. https://doi.org/10.1016/j.comnet.2022.109206

  51. Abououf M, Otrok H, Mizouni R, Singh S, Damiani E (2021) How artificial intelligence and mobile crowd sourcing are inextricably intertwined. IEEE Network 35(3):252–258. https://doi.org/10.1109/MNET.011.2000516

    Article  Google Scholar 

  52. Zhao Y, Gong X, Lin F, Chen X (2023) Data poisoning attacks and defenses in dynamic crowdsourcing with online data quality learning. IEEE Trans Mob Comput 22(5):2569–2581. https://doi.org/10.1109/TMC.2021.3133365

    Article  Google Scholar 

  53. Wang X, Umehira M, Akimoto M, Han B, Zhou H (2023) Green spectrum sharing framework in B5G era by exploiting crowdsensing. IEEE Trans Green Commun Netw 7(2):916–927. https://doi.org/10.1109/TGCN.2022.3186282

    Article  Google Scholar 

  54. Kantipudi MVVP, Aluvalu R, Velamuri S (2023) An intelligent approach of intrusion detection in mobile crowd sourcing systems in the context of IoT Based SMART City. Smart Sci 11(1):234–240. https://doi.org/10.1080/23080477.2022.2117889

    Article  Google Scholar 

  55. Pimpinella A, Repossi M, Redondi AEC (2022) Unsatisfied today, satisfied tomorrow: A simulation framework for performance evaluation of crowdsourcing-based network monitoring. Comput Commun 182:184–197. https://doi.org/10.1016/j.comcom.2021.11.004

    Article  Google Scholar 

  56. Zhao Y et al (2021) Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices. IEEE Internet Things J 8(3):1817–1829. https://doi.org/10.1109/JIOT.2020.3017377

    Article  Google Scholar 

  57. Hu R, Guo Y, Li H, Pei Q, Gong Y (2020) Personalized federated learning with differential privacy. IEEE Internet Things J 7(10):9530–9539. https://doi.org/10.1109/JIOT.2020.2991416

    Article  Google Scholar 

  58. Wu Z, Wu X, Long Y (2022) Prediction based semi-supervised online personalized federated learning for indoor localization. IEEE Sens J 22(11):10640–10654. https://doi.org/10.1109/JSEN.2022.3165042

    Article  Google Scholar 

  59. Lu L, Neale N, Line ND, Bonn M (2022) Improving data quality using amazon mechanical Turk through platform setup. Cornell Hosp Q 63(2):231–246. https://doi.org/10.1177/19389655211025475

    Article  Google Scholar 

  60. de Winter JCF, Kyriakidis M, Dodou D, Happee R (2015) Using crowdflower to study the relationship between self-reported violations and traffic accidents. Procedia Manuf 3:2518–2525. https://doi.org/10.1016/j.promfg.2015.07.514

    Article  Google Scholar 

  61. M. Abououf, S. Singh, H. Otrok, R. Mizouni, E. Damiani (2021) Machine learning in mobile crowd sourcing: a behavior-based recruitment model. ACM Trans Internet Technol 22(1):Article 16. https://doi.org/10.1145/3451163

  62. Wen J, Zhang Z, Lan Y, Cui Z, Cai J, Zhang W (2023) A survey on federated learning: challenges and applications. Int J Mach Learn Cybernet 14(2):513–535. https://doi.org/10.1007/s13042-022-01647-y

    Article  Google Scholar 

  63. Tong Y, Wang Y, Shi D (2020) Federated learning in the lens of crowdsourcing. IEEE Data Eng Bull 43(3):26–36

    Google Scholar 

  64. Lim WYB et al (2020) Federated learning in mobile edge networks: A comprehensive survey. IEEE Commun Surv Tutor 22(3):2031–2063

    Article  Google Scholar 

  65. Liu X, Chen H, Liu Y, Wei W, Xue H, Xia F (2024) Multitask data collection with limited budget in edge-assisted mobile crowdsensing. IEEE Internet Things J 11(9):16845–16858. https://doi.org/10.1109/JIOT.2024.3364239

    Article  Google Scholar 

  66. Yang X, Xu Y, Zhou Y, Song S, Wu Y (2022) Demand-aware mobile bike-sharing service using collaborative computing and information fusion in 5G IoT environment. Digit Commun Netw 8(6):984–994. https://doi.org/10.1016/j.dcan.2022.06.004

    Article  Google Scholar 

  67. Chang JC, Amershi S, Kamar E (2017) Revolt: Collaborative Crowdsourcing for Labeling Machine Learning Datasets," presented at the Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver, Colorado, USA. [Online]. Available: https://doi.org/10.1145/3025453.3026044

  68. Zheng Y, Li G, Li Y, Shan C, Cheng R (2017) Truth inference in crowdsourcing: is the problem solved? Proc VLDB Endow 10(5):541–552. https://doi.org/10.14778/3055540.3055547

    Article  Google Scholar 

  69. Kurve A, Miller DJ, Kesidis G (2015) Multicategory crowdsourcing accounting for variable task difficulty, worker skill, and worker intention. IEEE Trans Knowl Data Eng 27(3):794–809. https://doi.org/10.1109/TKDE.2014.2327026

    Article  Google Scholar 

  70. Sayin B, Krivosheev E, Yang J, Passerini A, Casati F (2021) A review and experimental analysis of active learning over crowdsourced data. Artif Intell Rev 54(7):5283–5305. https://doi.org/10.1007/s10462-021-10021-3

    Article  Google Scholar 

  71. Kosmala M, Hufkens K, Richardson AD (2018) Integrating camera imagery, crowdsourcing, and deep learning to improve high-frequency automated monitoring of snow at continental-to-global scales. PLoS One 13(12):e0209649. https://doi.org/10.1371/journal.pone.0209649

    Article  Google Scholar 

  72. Singh P, Jagyasi B, Rai N, Gharge S (2014) Decision tree based mobile crowdsourcing for agriculture advisory system," in 2014 Annual IEEE India Conference (INDICON), 11–13:1–6. https://doi.org/10.1109/INDICON.2014.7030560

  73. Lu Z, Chan K, Pu S, Porta TL (2019) Crowdvision: a computing platform for video crowdprocessing using deep learning. IEEE Trans Mob Comput 18(7):1513–1526. https://doi.org/10.1109/TMC.2018.2864212

    Article  Google Scholar 

  74. Yasmin R, Hassan MM, Grassel JT, Bhogaraju H, Escobedo AR, Fuentes O (2022) Improving Crowdsourcing-Based Image Classification Through Expanded Input Elicitation and Machine Learning," (in English). Front Artif Intell Original Res 5. https://doi.org/10.3389/frai.2022.848056

  75. Wang Y et al (2022) A survey on deploying mobile deep learning applications: A systemic and technical perspective. Digit Commun Netw 8(1):1–17. https://doi.org/10.1016/j.dcan.2021.06.001

    Article  Google Scholar 

  76. Mao Y, You C, Zhang J, Huang K, Letaief KB (2017) A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutor 19(4):2322–2358. https://doi.org/10.1109/COMST.2017.2745201

    Article  Google Scholar 

  77. Kurtah P, Takun Y, Nagowah L (2019) Disease Propagation Prediction using Machine Learning for Crowdsourcing Mobile Applications, in 2019 7th International Conference on Information and Communication Technology (ICoICT), 24–26 July 2019, pp. 1–6, https://doi.org/10.1109/ICoICT.2019.8835381

  78. Yadav K, Kumaraguru P, Goyal A, Gupta A, Naik V (2011) SMSAssassin: crowdsourcing driven mobile-based system for SMS spam filtering, presented at the Proceedings of the 12th Workshop on Mobile Computing Systems and Applications, Phoenix, Arizona. [Online]. Available: https://doi.org/10.1145/2184489.2184491

  79. Hamm J, Champion AC, Chen G, Belkin M, Xuan D (2015) Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices, in 2015 IEEE 35th International Conference on Distributed Computing Systems, 29 June-2 July 2015, pp. 11–20, https://doi.org/10.1109/ICDCS.2015.10

  80. Jiang L, Tan R, Lou X, Lin G (2019) On lightweight privacy-preserving collaborative learning for internet-of-things objects, presented at the Proceedings of the International Conference on Internet of Things Design and Implementation, Montreal, Quebec, Canada. [Online]. Available: https://doi.org/10.1145/3302505.3310070

  81. Xu P, Zhu X, Clifton DA (2023) Multimodal learning with transformers: a survey. IEEE Trans Pattern Anal Mach Intell 45(10):12113–12132. https://doi.org/10.1109/TPAMI.2023.3275156

    Article  Google Scholar 

  82. Gao H, Wang X, Wei W, Al-Dulaimi A, Xu Y (2024) Com-DDPG: task offloading based on multiagent reinforcement learning for information-communication-enhanced mobile edge computing in the internet of vehicles. IEEE Trans Veh Technol 73(1):348–361. https://doi.org/10.1109/TVT.2023.3309321

    Article  Google Scholar 

  83. Wu H et al (2015) Combining Machine Learning and Crowdsourcing for Better Understanding Commodity Reviews. Proc AAAI Conf Artif Intell 29(1), 03/04, https://doi.org/10.1609/aaai.v29i1.9725

  84. Sun Y, Tan W (2019) A trust-aware task allocation method using deep q-learning for uncertain mobile crowdsourcing. Human-centric Comput Inf Sci 9(1):25. https://doi.org/10.1186/s13673-019-0187-4

    Article  Google Scholar 

  85. Chen J, Cao B, Peng Z, Xie Z, Liu S, Peng Q (2024) TN-MR: topic-aware neural network-based mobile application recommendation. Int J Web Inf Syst 20(2):159–175. https://doi.org/10.1108/IJWIS-10-2023-0205

    Article  Google Scholar 

  86. Federation D (2023) Data Federation." (accessed 2023)

  87. McMahan B, Moore E, Ramage D, Hampson S, y Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data," in Artificial intelligence and statistics, PMLR, 1273–1282

  88. Pham QV, Zeng M, Huynh-The T, Han Z, Hwang WJ (2022) Aerial access networks for federated learning: applications and challenges. IEEE Netw 36(3):159–166. https://doi.org/10.1109/MNET.013.2100311

    Article  Google Scholar 

  89. Zhang C, Xie Y, Bai H, Yu B, Li W, Gao Y (2021) A survey on federated learning. Knowl-Based Syst 216:106775

    Article  Google Scholar 

  90. Saha S, Ahmad T (2021) Federated transfer learning: concept and applications. Intell Artif 15:35–44. https://doi.org/10.3233/IA-200075

    Article  Google Scholar 

  91. Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: Concept and applications. ACM Trans Intell Syst Technol (TIST) 10(2):1–19

    Article  Google Scholar 

  92. Zheng J, Ni W, Tian H, Gündüz D, Quek TQS, Han Z (2023) Semi-Federated Learning: Convergence Analysis and Optimization of a Hybrid Learning Framework. IEEE Trans Wireless Commun 22(12):9438–9456. https://doi.org/10.1109/TWC.2023.3270908

    Article  Google Scholar 

  93. Mawuli CB et al (2023) Semi-supervised federated learning on evolving data streams. Inf Sci 643:119235. https://doi.org/10.1016/j.ins.2023.119235

    Article  Google Scholar 

  94. Li D et al (2023) Semi-centralized federated learning network for low-dose CT imaging (SPIE Medical Imaging). SPIE

  95. Tang F, Liang S, Ling G, Shan J (2023) IHVFL: a privacy-enhanced intention-hiding vertical federated learning framework for medical data. Cybersecurity 6(1):37. https://doi.org/10.1186/s42400-023-00166-9

    Article  Google Scholar 

  96. Kim H, Park J, Bennis M, Kim SL (2020) Blockchained on-device federated learning. IEEE Commun Lett 24(6):1279–1283. https://doi.org/10.1109/LCOMM.2019.2921755

    Article  Google Scholar 

  97. Warnat-Herresthal S et al (2021) Swarm Learning for decentralized and confidential clinical machine learning. Nature 594(7862):265–270. https://doi.org/10.1038/s41586-021-03583-3

    Article  Google Scholar 

  98. Zhang P, Wang C, Jiang C, Han Z (2021) Deep reinforcement learning assisted federated learning algorithm for data management of IIoT. IEEE Trans Industr Inf 17(12):8475–8484. https://doi.org/10.1109/TII.2021.3064351

    Article  Google Scholar 

  99. Ghosh A, Chung J, Yin D, Ramchandran K (2022) An efficient framework for clustered federated learning. IEEE Trans Inf Theory 68(12):8076–8091. https://doi.org/10.1109/TIT.2022.3192506

    Article  MathSciNet  Google Scholar 

  100. Shang F, Liu Y, Cheng J, Zhuo J (2017) Fast stochastic variance reduced gradient method with momentum acceleration for machine learning, arXiv preprint arXiv:1703.07948

  101. Li Q, Wen Z, He B (2020) Practical federated gradient boosting decision trees. Proc AAAI Conf Artif Intell 34(04):4642–4649

    Google Scholar 

  102. Cheng K et al (2021) Secureboost: A lossless federated learning framework. IEEE Intell Syst 36(6):87–98

    Article  Google Scholar 

  103. Liu Y et al (2022) Federated forest. IEEE Trans Big Data 8(03):843–854. https://doi.org/10.1109/TBDATA.2020.2992755

    Article  Google Scholar 

  104. Wang H, Yurochkin M, Sun Y, Papailiopoulos D, Khazaeni Y (2020) Federated learning with matched averaging, in International Conference on Learning Representations

  105. Mills J, Hu J, Min G (2022) Multi-task federated learning for personalised deep neural networks in edge computing. IEEE Trans Parallel Distrib Syst 33(3):630–641. https://doi.org/10.1109/TPDS.2021.3098467

    Article  Google Scholar 

  106. da Silva LGF, Sadok DF, Endo PT (2023) Resource optimizing federated learning for use with IoT: A systematic review. J Parallel Distrib Comput 175:92–108

    Article  Google Scholar 

  107. Li L, Fan Y, Tse M, Lin K-Y (2020) A review of applications in federated learning. Comput Ind Eng 149:106854

    Article  Google Scholar 

  108. Karimireddy SP, Kale S, Mohri M, Reddi S, Stich S, Suresh AT (2020) Scaffold: Stochastic controlled averaging for federated learning, in International Conference on Machine Learning, PMLR. 5132–5143

  109. Li Q, Diao Y, Chen Q, He B (2022) Federated learning on non-iid data silos: An experimental study, in 2022 IEEE 38th International Conference on Data Engineering (ICDE) IEEE, 965–978

  110. Xu J, Jin Y, Du W, Gu S (2021) A federated data-driven evolutionary algorithm. Knowl-Based Syst 233:107532

    Article  Google Scholar 

  111. Cohen G, Afshar S, Tapson J, Van Schaik A (2017) EMNIST: Extending MNIST to handwritten letters, in 2017 international joint conference on neural networks (IJCNN), IEEE, 2921–2926

  112. Yurochkin M, Agarwal M, Ghosh S, Greenewald K, Hoang N, Khazaeni Y (2019) Bayesian nonparametric federated learning of neural networks, in International conference on machine learning, PMLR, 7252–7261

  113. Garcia-Molina H, Joglekar M, Marcus A, Parameswaran A, Verroios V (2016) Challenges in data crowdsourcing. IEEE Trans Knowl Data Eng 28(4):901–911

    Article  Google Scholar 

  114. Liu Y, Kang Y, Xing C, Chen T, Yang Q (2020) A secure federated transfer learning framework. IEEE Intell Syst 35(4):70–82

    Article  Google Scholar 

  115. Sattler F, Wiedemann S, Müller K-R, Samek W (2019) Robust and communication-efficient federated learning from non-iid data. IEEE Trans Neural Netw Learn Syst 31(9):3400–3413

    Article  Google Scholar 

  116. Nguyen DC, Ding M, Pathirana PN, Seneviratne A, Li J, Poor HV (2021) Federated Learning for Internet of Things: A Comprehensive Survey. IEEE Commun Surv Tutor 23(3):1622–1658. https://doi.org/10.1109/COMST.2021.3075439

    Article  Google Scholar 

  117. Khan LU, Saad W, Han Z, Hossain E, Hong CS (2021) Federated Learning for internet of things: recent advances, taxonomy, and open challenges. IEEE Commun Surv Tutor 23(3):1759–1799. https://doi.org/10.1109/COMST.2021.3090430

    Article  Google Scholar 

  118. Zhang T, Gao L, He C, Zhang M, Krishnamachari B, Avestimehr AS (2022) Federated learning for the internet of things: applications, challenges, and opportunities. IEEE Internet Things Magazine 5(1):24–29. https://doi.org/10.1109/IOTM.004.2100182

    Article  Google Scholar 

  119. Pandya S et al (2023) Federated learning for smart cities: A comprehensive survey. Sustain Energy Technol Assess 55:102987. https://doi.org/10.1016/j.seta.2022.102987

    Article  Google Scholar 

  120. Aledhari M, Razzak R, Parizi RM, Saeed F (2020) Federated learning: A survey on enabling technologies, protocols, and applications. IEEE Access 8:140699–140725

    Article  Google Scholar 

  121. Niknam S, Dhillon HS, Reed JH (2020) Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges. IEEE Commun Mag 58(6):46–51. https://doi.org/10.1109/MCOM.001.1900461

    Article  Google Scholar 

  122. Shome D, Waqar O, Khan WU (2022) Federated learning and next generation wireless communications: A survey on bidirectional relationship. Trans Emerg Telecommun Technol 33(7):e4458. https://doi.org/10.1002/ett.4458

    Article  Google Scholar 

  123. Nguyen DC et al (2022) Federated learning for smart healthcare: A survey. ACM Comput Surv (CSUR) 55(3):1–37

    Article  Google Scholar 

  124. Cheng Y, Liu Y, Chen T, Yang Q (2020) Federated learning for privacy-preserving AI. Commun ACM 63(12):33–36

    Article  Google Scholar 

  125. Zheng W, Yan L, Gou C, Wang F-Y (2021) Federated meta-learning for fraudulent credit card detection, presented at the Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Yokohama, Japan

  126. Leroy D, Coucke A, Lavril T, Gisselbrecht T, Dureau J (2019) Federated learning for keyword spotting, in ICASSP 2019–2019 IEEE international conference on acoustics, speech and signal processing (ICASSP),IEEE, 6341–6345

  127. Chen W, Milosevic Z, Rabhi FA, Berry A (2023) Real-Time Analytics: Concepts, Architectures, and ML/AI Considerations. IEEE Access 11:71634–71657. https://doi.org/10.1109/ACCESS.2023.3295694

    Article  Google Scholar 

  128. Liao Z, Ai J, Liu S, Zhang Y, Liu S (2023) Blockchain-based mobile crowdsourcing model with task security and task assignment. Expert Syst Appl 211:118526. https://doi.org/10.1016/j.eswa.2022.118526

    Article  Google Scholar 

  129. Wu W et al (2024) Autonomous crowdsensing: operating and organizing crowdsensing for sensing automation. IEEE Trans Intell Veh 9(3):4254–4258. https://doi.org/10.1109/TIV.2024.3355508

    Article  Google Scholar 

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Funding

This research was supported by the Fujian Provincial Natural Science Foundation of China (Grant No. 2022J05291). The second author received partial support from ACU to complete this research, under grant number CAWGS – 905737–111.

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Authors and Affiliations

  1. School of Software Engineering, Xiamen University of Technology, Xiamen, 361024, Fujian, China

    Weisi Chen & Xu Zhang

  2. Peter Faber Business School, Australian Catholic University, North Sydney, NSW, 2060, Australia

    Walayat Hussain

  3. Higher Colleges of Technology, Abu Dhabi, United Arab Emirates

    Islam Al-Qudah

  4. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

    Ghazi Al-Naymat

Authors
  1. Weisi Chen
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  2. Walayat Hussain
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  3. Islam Al-Qudah
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  4. Ghazi Al-Naymat
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  5. Xu Zhang
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Contributions

All authors contributed to this review paper. Weisi Chen had the idea for the article. The literature search was performed by Weisi Chen, Walayat Hussain, Islam Al-Qudah, and Ghazi Al-Naymat. Relevant data analysis was conducted by Weisi Chen, Walayat Hussain, Islam Al-Qudah, Ghazi Al-Naymat, and Xu Zhang. The first draft of the manuscript was written by Weisi Chen, Walayat Hussain, Islam Al-Qudah, and Ghazi Al-Naymat, and Weisi Chen and Walayat Hussain prepared all revised versions. All authors read and approved the final manuscript. Walayat Hussain, Islam Al-Qudah, and Ghazi Al-Naymat contributed equally to this work.

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Correspondence to Weisi Chen.

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Chen, W., Hussain, W., Al-Qudah, I. et al. Intersection of machine learning and mobile crowdsourcing: a systematic topic-driven review. Pers Ubiquit Comput (2024). https://doi.org/10.1007/s00779-024-01820-w

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