survey

A Survey on Collaborative Learning for Intelligent Autonomous Systems

Authors:

Julio C. S. Dos Anjos,

Kassiano J. Matteussi,

Fernanda C. Orlandi,

Jorge L. V. Barbosa,

Jorge Sá Silva,

Luiz F. Bittencourt,

Cláudio F. R. GeyerAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 4

Article No.: 98, Pages 1 - 37

https://doi.org/10.1145/3625544

Published: 10 November 2023 Publication History

Abstract

This survey examines approaches to promote Collaborative Learning in distributed systems for emergent Intelligent Autonomous Systems (IAS). The study involves a literature review of Intelligent Autonomous Systems based on Collaborative Learning, analyzing aspects in four dimensions: computing environment, performance concerns, system management, and privacy concerns, mapping the significant requirements of systems to the emerging Artificial intelligence models. Furthermore, the article explores Collaborative Learning Taxonomy for IAS to demonstrate the correlation between IoT, Big Data, and Human-in-the-Loop. Several technological open issues exist in the aforementioned domains (such as in applications of autonomous driving, robotics in healthcare, cyber security, and others) to effectively achieve the future deployment of Intelligent Autonomous Systems. This Survey aims to organize concepts around IAS, indicating the approaches used to extract knowledge from data in Collaborative Learning for IAS, and identifying open issues. Moreover, it presents a guide to overcoming the existing challenges in decision-making mechanisms with IAS, providing a holistic vision of Big Data and Human-in-the-Loop.

References

[1]

Haftay Gebreslasie Abreha, Mohammad Hayajneh, and Mohamed Adel Serhani. 2022. Federated learning in edge computing: A systematic survey. Sensors 22, 2 (Jan.2022), 1–45. DOI:

[2]

Spark Apache. 2022. Apache Spark. Retrieved from https://spark.apache.org/

[3]

Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. 2017. Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine 34, 6 (Nov.2017), 26–38. DOI:

[4]

Sagar Behere, Fredrik Asplund, Andreas Söderberg, and Martin Törngren. 2016. Architecture challenges for intelligent autonomous machines. In Intelligent Autonomous Systems 13 (Advances in Intelligent Systems and Computing (AISC)). Emanuele Menegatti, Nathan Michael, Karsten Berns, and Hiroaki Yamaguchi (Eds.), Vol. 302. Springer International Publishing, Cham, 1669–1681. DOI:

[5]

Yoshua Bengio, Yann Lecun, and Geoffrey Hinton. 2021. Deep learning for AI. Commun. ACM 64, 7 (June2021), 58–65. DOI:

Digital Library

[6]

Anders E. Braten, Frank A. Kraemer, and David Palma. 2020. Autonomous IoT device management systems: Structured review and generalized cognitive model. IEEE Internet of Things Journal 8, 6 (Nov.2020), 1–16. DOI:

[7]

Lucian Busoniu, Robert Babuska, and Bart De Schutter. 2008. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38, 2 (Feb.2008), 156–172. DOI:

Digital Library

[8]

Hongyan Chang, Virat Shejwalkar, Reza Shokri, and Amir Houmansadr. 2019. Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer. CoRR abs/1912.11279, 1–16. http://arxiv.org/abs/1912.11279

[9]

C. L. P. Chen and Z. Liu. 2018. Broad learning system: An effective and efficient incremental learning system without the need for deep architecture. IEEE Transactions on Neural Networks and Learning Systems 29, 1 (Jan.2018), 10–24. DOI:

[10]

Min Chen and Yixue Hao. 2020. Label-less learning for emotion cognition. IEEE Transactions on Neural Networks and Learning Systems 31, 7 (July2020), 2430–2440. DOI:

[11]

Migzhe Chen, H. Vicent Poor, Walid Saad, and Shuguang Cui. 2020. Wireless communications for collaborative federated learning. IEEE Communications Magazine 58, 12 (Dec.2020), 48–54. DOI:

[12]

Shuzhen Chen, Youming Tao, Dongxiao Yu, Feng Li, Bei Gong, and Xiuzhen Cheng. 2021. Privacy-preserving collaborative learning for multiarmed bandits in IoT. IEEE Internet of Things Journal 8, 5 (March2021), 3276–3286. DOI:

[13]

Yujing Chen, Yue Ning, Martin Slawski, and Huzefa Rangwala. 2020. Asynchronous online federated learning for edge devices with non-IID data. In 2020 IEEE International Conference on Big Data (Big Data). IEEE Computer Society, 15–24. DOI:

[14]

Zheyi Chen, Pu Tian, Weixian Liao, and Wei Yu. 2021. Zero knowledge clustering based adversarial mitigation in heterogeneous federated learning. IEEE Transactions on Network Science and Engineering 8, 2 (June2021), 1070–1083. DOI:

[15]

Te-Chuan Chiu, Yuan-Yao Shih, Ai-chun Pang, Chieh-Sheng Wang, Wei Weng, and Chun-Ting Chou. 2020. Semisupervised distributed learning with non-IID data for AIoT service platform. IEEE Internet of Things Journal 7, 10 (May2020), 9266–9277. DOI:

[16]

Luca Corinzia, Ami Beuret, and Joachim M. Buhmann. 2021. Variational federated multi-task learning. CoRR abs/1906.06268, 1–12. https://arxiv.org/abs/1906.06268

[17]

Maartje MA De Graaf and Bertram F Malle. 2017. How people explain action (and autonomous intelligent systems should too). In 2017 AAAI Fall Symposium Series. The AAAI Press, 1–8.

[18]

Louise A. Dennis and Michael Fisher. 2020. Verifiable self-aware agent-based autonomous systems. Proc. IEEE 108, 7 (July2020), 1011–1026. DOI:

[19]

Joris Dinneweth, Abderrahmane Boubezoul, René Mandiau, and Stéphane Espié. 2022. Multi-agent reinforcement learning for autonomous vehicles: A survey. Autonomous Intelligent Systems 2, 27 (Nov.2022), 1–12. showISSN2730-616X DOI:

[20]

Hao Dong, Zihan Ding, and Shanghang Zhang (Eds.). 2020. Deep Reinforcement Learning—Fundamentals, Research and Applications. Springer Nature Singapore, Gateway East, Singapore. 526 pages.

[21]

Julio C. S. dos Anjos, João L. G. Gross, Kassiano J. Matteussi, Gabriel V. González, Valderi R. Q. Leithardt, and Claudio F. R. Geyer. 2021. An algorithm to minimize energy consumption and elapsed time for IoT workloads in a hybrid architecture. Sensors 21, 9 (April2021), 1–20. DOI:

[22]

J. C. S. dos Anjos, K. J. Matteussi, P. R. R. De Souza, G. J. A. Grabher, G. A. Borges, J. L. V. Barbosa, G. V. González, V. R. Q. Leithardt, and C. F. R. Geyer. 2020. Data processing model to perform big data analytics in hybrid infrastructures. IEEE Access 8 (Sept.2020), 170281–170294. DOI:

[23]

Julio C. S. dos Anjos, Kassiano J Matteussi, Paulo RR de Souza Jr, Alexandre da Silva Veith, Gilles Fedak, Jorge Luis Victória Barbosa, and Claudio F. R. Geyer. 2018. Enabling strategies for big data analytics in hybrid infrastructures. HPCS—-International Conference on High Performance Computing and Simulation, 869–876. DOI:

[24]

Zhaoyang Du, Celimuge Wu, Tsutomu Yoshinaga, Kok-lim A. Yau, Yusheng Ji, and Jie Li. 2020. Federated learning for vehicular internet of things: Recent advances and open issues. IEEE Open Journal of the Computer Society 1 (May2020), 45–61. DOI:

[25]

Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, and Kunal Talwar. 2019. Semi-cyclic stochastic gradient descent. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research). Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. PMLR, 1764–1773.

[26]

Haya Elayan, Moayad Aloqaily, and Mohsen Guizani. 2021. Sustainability of healthcare data analysis IoT-based systems using deep federated learning. IEEE Internet of Things Journal 9, 10 (2021), 1–9.

[27]

Sizheng Fan, Hongbo Zhang, Yuchen Zeng, and Wei Cai. 2021. Hybrid blockchain-based resource trading system for federated learning in edge computing. IEEE Internet of Things Journal 8, 4 (Feb.2021), 2252–2264. DOI:

[28]

Peter Farhat, Hani Sami, and Azzam Mourad. 2020. Reinforcement R-learning model for time scheduling of on-demand fog placement. The Journal of Supercomputing 76, 1 (2020), 388–410. DOI:

Digital Library

[29]

Lei Feng, Yiqi Zhao, Shaoyong Guo, Xuesong Qiu, Wenjing Li, and Peng Yu. 2021. Blockchain-based asynchronous federated learning for Internet of Things. IEEE Trans. Comput. 71, 5 (April2021), 1–12. DOI:

[30]

Nuwan Ferdinand and Stark Draper. 2018. Anytime Stochastic Gradient Descent: A Time to Hear from all the Workers. Cornell University, ArXiv, 1–10. https://arxiv.org/abs/1810.02976

[31]

Nuwan Ferdinand, Benjamin Gharachorloo, and Stark C. Draper. 2017. Anytime exploitation of stragglers in synchronous stochastic gradient descent. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE Computer Society, 141–146. DOI:

[32]

J. Fernandes, D. Raposo, N. Armando, S. Sinche, J. Sá Silva, A. Rodrigues, V. Pereira, H. Gonçalo Oliveira, Luís Macedo, and F. Boavida. 2020. ISABELA—A socially-aware human-in-the-loop advisor system. Online Social Networks and Media 16 (March2020), 100060. DOI:

[33]

Anmin Fu, Xianglong Zhang, Naixue Xiong, Yansong Gao, Huaqun Wang, and Jing Zhang. 2020. VFL: A verifiable federated learning with privacy-preserving for big data in industrial IoT. IEEE Transactions on Industrial Informatics 18, 5 (Nov.2020), 1–11. DOI:

[34]

Xin Gao, Donghua Wang, Jun Zhang, Qing Liao, and Bin Liu. 2019. iRBP-Motif-PSSM: Identification of RNA-binding proteins based on collaborative learning. IEEE Access 7 (Nov.2019), 168956–168962. DOI:

[35]

Ankan Ghosh, Osman Khalid, Rao N. B. Rais, Amjad Rehman, Saif U. R. Malik, and Imran A. Khan. 2019. Data offloading in IoT environments: Modeling, analysis, and verification. EURASIP Journal on Wireless Communications and Networking 2019, 59 (March2019), 1–23. DOI:

[36]

João Luiz Grave Gross, Kassiano José Matteussi, Julio C. S. dos Anjos, and Cláudio Fernando Resin Geyer. 2020. A dynamic cost model to minimize energy consumption and processing time for IoT tasks in a mobile edge computing environment. In Service-Oriented Computing. Eleanna Kafeza, Boualem Benatallah, Fabio Martinelli, Hakim Hacid, Athman Bouguettaya, and Hamid Motahari (Eds.), Vol. 12571. Springer International Publishing, Cham, 101–109. DOI:

Digital Library

[37]

Deepti Gupta, Olumide Kayode, Smriti Bhatt, Maanak Gupta, and Ali Saman Tosun. 2020. Learner’s dilemma: IoT devices training strategies in collaborative deep learning. In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). IEEE Computer Society, 1–6. DOI:

[38]

Deepti Gupta, Olumide Kayode, Smriti Bhatt, Maanak Gupta, and Ali S. Tosun. 2020. Learner’s dilemma: IoT devices training strategies in collaborative deep learning. In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). IEEE Computer Society, 1–6. DOI:

[39]

Ido Hakimi, Saar Barkai, Moshe Gabel, and Assaf Schuster. 2020. Taming Momentum in a Distributed Asynchronous Environment. Cornell University, 1–18. https://arxiv.org/abs/1907.11612

[40]

Talal Halabi and Mohammad Zulkernine. 2019. Trust-based cooperative game model for secure collaboration in the internet of vehicles. In ICC 2019–2019 IEEE International Conference on Communications (ICC). IEEE Computer Society, 1–6. DOI:

[41]

Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu. 2021. Liquid time-constant networks. In Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21). 7657–7666.

[42]

Alexander Heimerl, Tobias Baur, Florian Lingenfelser, Johannes Wagner, and Elisabeth André. 2019. NOVA—A tool for eXplainable cooperative machine learning. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE Computer Society, 109–115.

[43]

Wenxing Hu, Biao Cai, iying Zhang, Vince D. Calhoun, and Yu-Ping Wang. 2019. Deep collaborative learning with application to the study of multimodal brain development. IEEE Transactions on Biomedical Engineering 66, 12 (March2019), 3346–3359. DOI:

[44]

Gaofeng Hua, Li Zhu, Jinsong Wu, Chunzi Shen, Linyan Zhou, and QingQing Lin. 2020. Blockchain-based federated learning for intelligent control in heavy haul railway. IEEE Access 8 (Sept.2020), 176830–176839. DOI:

[45]

Mohsen Imani, Yeseong Kim, Sadegh Riazi, John Messerly, Patric Liu, Farinaz Koushanfar, and Tajana Rosing. 2019. A framework for collaborative learning in secure high-dimensional space. In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). IEEE Computer Society, 435–446. DOI:

[46]

Qi Jia, Linke Guo, Yuguang Fang, and Guirong Wang. 2019. Efficient privacy-preserving machine learning in hierarchical distributed system. IEEE Transactions on Network Science and Engineering 6, 4 (Dec.2019), 599–612. DOI:

[47]

Linshan Jiang, Rui Tan, Xin Lou, and Guosheng Lin. 2019. On lightweight privacy-preserving collaborative learning for internet-of-things objects. In Proceedings of the International Conference on Internet of Things Design and Implementation (IoTDI’19). ACM, New York, NY, 70–81. DOI:

Digital Library

[48]

Sohag Kabir and Yiannis Papadopoulos. 2020. Computational intelligence for safety assurance of cooperative systems of systems. Computer 53, 12 (Nov.2020), 24–34. DOI:

Digital Library

[49]

P. Kanerva. 2019. Computing with high-dimensional vectors. IEEE Design Test 36, 3 (Dec.2019), 7–14. DOI:

[50]

Khaled M. Khalil, M. Abdel-Aziz, Taymour T. Nazmy, and Abdel-Badeeh M. Salem. 2015. Machine learning algorithms for multi-agent systems. In Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication (IPAC’15). ACM, New York, NY, Article 59, 5 pages. DOI:

Digital Library

[51]

Tran V. Khoa, Yuris M. Saputra, Dinh T. Hoang, Nguyen L. Trung, Diep Nguyen, Nguyen V. Ha, and Eryk Dutkiewicz. 2020. Collaborative learning model for cyberattack detection systems in IoT industry 4.0. In 2020 IEEE Wireless Communications and Networking Conference (WCNC). 1–6. DOI:

Digital Library

[52]

Barbara A. Kitchenham. 2012. Systematic review in software engineering: Where we are and where we should be going. In Proceedings of the 2nd International Workshop on Evidential Assessment of Software Technologies (EAST’12). Association for Computing Machinery, New York, NY, 1–2. DOI:

Digital Library

[53]

Aexandros Kouris, Stylianos I. Venieris, and Christos-Savvas Bouganis. 2019. Towards efficient on-board deployment of DNNs on intelligent autonomous systems. In 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). IEEE Computer Society, 568–573. DOI:

[54]

A. Gilad Kusne, Heshan Yu, Changming Wu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Suchismita Sarker, Corey Oses, Cormac Toher, Stefano Curtarolo, Albert V. Davydov, Ritesh Agarwal, Leonid A. Bendersky, Mo Li, Apurva Mehta, and Ichiro Takeuchi. 2020. On-the-fly closed-loop materials discovery via Bayesian active learning. Nature Communications Volume 11, 5966(2020) (Nov.2020), 1–11. DOI:

[55]

Hung M. La, Ronny Lim, and Wwihua Sheng. 2015. Multirobot cooperative learning for predator avoidance. IEEE Transactions on Control Systems Technology 23, 1 (April2015), 52–63. DOI:

[56]

John E. Laird, Christian Lebiere, and Paul S. Rosenbloom. 2017. A standard model of the mind: Toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. AI Magazine 38, 4 (Dec.2017), 13–26. DOI:

Digital Library

[57]

Lei Lei, Yue Tan, Kan Zheng, Shiwen Liu, Kuan Zhang, and Xuemin Shen. 2020. Deep reinforcement learning for autonomous Internet of Things: Model, applications and challenges. IEEE Communications Surveys Tutorials 22, 3 (April2020), 1722–1760. DOI:

[58]

Tian Li, Aniy Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37, 3 (May2020), 50–60. DOI:

[59]

Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated optimization in heterogeneous networks. In Proceedings of Machine Learning and Systems 2020, MLSys 2020, Inderjit S. Dhillon, Dimitris S. Papailiopoulos, and Vivienne Sze (Eds.), Vol. 2. mlsys.org, Austin, TX, 429–450.

[60]

Wenhui Li, Weizhi Nie, and Yuting Su. 2018. Human action recognition based on selected spatio-temporal features via bidirectional LSTM. IEEE Access 6 (Aug.2018), 44211–44220. DOI:

[61]

Ying Lin, Kaibo Liu, Eunshin Byon, Xiaoning Qian, Shan Liu, and Shuai Huang. 2018. A collaborative learning framework for estimating many individualized regression models in a heterogeneous population. IEEE Transactions on Reliability 67, 1 (March2018), 328–341. DOI:

[62]

Guozhi Liu, Fei Dai, Bi Huang, Zhenping Qiang, Shuai Wang, and Lecheng Li. 2022. A collaborative computation and dependency-aware task offloading method for vehicular edge computing: A reinforcement learning approach. Journal of Cloud Computing 11, 68 (Oct.2022), 1–15. DOI:

Digital Library

[63]

Ganapathy Mani, Bhargava Bharat, Basavesh Shivakumar, and Jason Kobes. 2018. Incremental learning through graceful degradations in autonomous systems. In 2018 IEEE International Conference on Cognitive Computing (ICCC). IEEE Computer Society, 25–32. DOI:

[64]

Gunasekaran Manogaran, Mamoun Alazab, P. Mohamed Shakeel, and Ching-Hsien Hsu. 2022. Blockchain assisted secure data sharing model for Internet of Things based smart industries. IEEE Transactions on Reliability 71, 1 (March2022), 348–358. DOI:

[65]

Kassiano J. Matteussi, Julio C. S. dos Anjos, Valderi R. Q. Leithardt, and Claudio F. R. Geyer. 2022. Performance evaluation analysis of spark streaming backpressure for data-intensive pipelines. Sensors 22, 13 (2022), 4756(1–28). DOI:

[66]

Kassiano J. Matteussi, Breno F. Zanchetta, Germano Bertoncello, Jobe Dos Santos, Julio C. S. Dos Anjos, and Claudio F. R. Geyer. 2019. Analysis and performance evaluation of deep learning on big data. In 2019 IEEE Symposium on Computers and Communications (ISCC) (IEEE ISCC 2019). IEEE Computer Society, 751–756. DOI:

[67]

Gerald Matthews, Peter A. Hancock, Jinchao Lin, April Rose Panganiban, Lauren E. Reinerman-Jones, James L. Szalma, and Ryan W. Wohleber. 2021. Evolution and revolution: Personality research for the coming world of robots, artificial intelligence, and autonomous systems. Personality and Individual Differences 169, 109969 (March2021), 1–11. DOI:

[68]

G. Matthews, L. E. Reinerman-Jones, D. J. Barber, G. Teo, R. W. Wohleber, J. Lin, and A. R. Panganiban. 2016. Resilient autonomous systems: Challenges and solutions. In 2016 Resilience Week (RWS). IEEE Computer Society, 208–213. DOI:

[69]

Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2019. Exploiting unintended feature leakage in collaborative learning. In 2019 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, 691–706. DOI:

[70]

Lingwu. Meng, Xiaoming. You, and Sheng Liu. 2020. Multi-colony collaborative ant optimization algorithm based on cooperative game mechanism. IEEE Access 8 (Sept.2020), 154153–154165. DOI:

[71]

M. Mohammadi and A. Al-Fuqaha. 2018. Enabling cognitive smart cities using big data and machine learning: Approaches and challenges. IEEE Communications Magazine 56, 2 (Feb.2018), 94–101. DOI:

Digital Library

[72]

Christopher Müller. 2022. World Robotics 2022 – Industrial Robots. Technical Report. Frankfurt, Germany. 98 pages.

[73]

S. Nahavandi. 2019. Trust in autonomous systems-iTrust lab: Future directions for analysis of trust with autonomous systems. IEEE Systems, Man, and Cybernetics Magazine 5, 3 (July2019), 52–59. DOI:

[74]

David Nunes, Jorge Sá Silva, and Fernando Boavida. 2018. A Practical Introduction to Human-in-the-Loop Cyber-Physical Systems. John Wiley & Sons. 289 pages.

Digital Library

[75]

D. S. Nunes, P. Zhang, and J. Sá Silva. 2015. A Survey on human-in-the-loop applications towards an internet of all. IEEE Communications Surveys Tutorials 17, 2 (Feb.2015), 944–965. DOI:

Digital Library

[76]

Y. V. Pant, H. Abbas, K. Mohta, R. A. Quaye, T. X. Nghiem, J. Devietti, and R. Mangharam. 2020. Anytime computation and control for autonomous systems. IEEE Transactions on Control Systems Technology 29, 2 (Mar2020), 1–12. DOI:

[77]

Nicolas Papernot, Martín Abadi, U. Erlingsson, I. Goodfellow, and Kunal Talwar. 2017. Semi-supervised knowledge transfer for deep learning from private training data. 5th International Conference on Learning Representations (ICLR’17), abs/1610.05755, 1–16. https://arxiv.org/abs/1610.05755

[78]

Jin-ho Park, Mikail Mohammed Salim, Jeong Hoon Jo, Jose Costa Sapalo Sicato, Shailendra Rathore, and Jong Hyuk Park. 2019. CIoT-net: A scalable cognitive IoT based smart city network architecture. Human-Centric Computing and Information Sciences 9, 29 (Aug.2019), 1–20. DOI:

Digital Library

[79]

European Parliament and Council EU. 2016. General data protection regulation. Official Journal of the European Union 59 (May2016), 1–149. Retrieved fromhttps://gdpr-info.eu/

[80]

Pijush Kanti Dutta Pramanik, Saurabh Pal, and Prasenjit Choudhury. 2018. Beyond Automation: The Cognitive IoT. Artificial Intelligence Brings Sense to the Internet of Things. Lecture Notes on Data Engineering and Communications Technologies, Vol. 14. Springer International Publishing, 1–37. DOI:

[81]

M. E. Pérez Hernández and S. Reiff-Marganiec. 2016. Towards a software framework for the autonomous Internet of Things. In 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE Computer Society, 220–227. DOI:

[82]

Attia Qammar, Ahmad Karim, Huansheng Ning, and Jianguo Ding. 2022. Securing federated learning with blockchain: A systematic literature review. Artificial Intelligence Review 56, 5 (Sept.2022), 1–35. DOI:

Digital Library

[83]

Shengsheng Qian, Tianzhu Zhang, and Changsheng Xu. 2018. Cross-domain collaborative learning via discriminative nonparametric Bayesian model. IEEE Transactions on Multimedia 20, 8 (Dec.2018), 2086–2099. DOI:

[84]

Yao Qin, Lorenzo Bruzzone, Biao. Li, and Yuanxin Ye. 2019. Cross-domain collaborative learning via cluster canonical correlation analysis and random walker for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57, 6 (Jan.2019), 3952–3966. DOI:

[85]

Sawsan A. Rahman, Hanine Tout, Hakima Ould-Slimane, Azzam Mourad, Chamseddine Talhi, and Mohsen Guizani. 2020. A survey on federated learning: The journey from centralized to distributed on-site learning and beyond. IEEE Internet of Things Journal 8, 7 (Oct.2020), 1–23. DOI:

[86]

Spark Rapids. 2022. RAPIDS—Accelerator for Apache Spark Leverages GPUs. Retrieved from https://nvidia.github.io/spark-rapids/

[87]

David Reinsel. 2019. How You Contribute to Today’s Growing DataSphere and Its Enterprise Impact. Retrieved from https://blogs.idc.com/2019/11/04/how-you-contribute-to-todays-growing-d%atasphere-and-its-enterprise-impact/

[88]

Stuart Russel and Peter Norving. 2021. Artificial Intelligence, A Modern Approach (4th ed.). Pearson Education Limited. 1066 pages.

[89]

M. Samaniego and R. Deters. 2017. Internet of smart things - IoST: Using blockchain and CLIPS to make things autonomous. In 2017 IEEE International Conference on Cognitive Computing (ICCC). IEEE Computer Society, 9–16. DOI:

[90]

Sujan Sarker, Lafifa Jamal, Syeda Faiza Ahmed, and Niloy Irtisam. 2021. Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. Robotics and Autonomous Systems 146, 103902 (Dec.2021), 1–18. DOI:

Digital Library

[91]

Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek. 2020. Robust and communication-efficient federated learning from non-i.i.d. data. IEEE Transactions on Neural Networks and Learning Systems 31, 9 (Sept.2020), 3400–3413. DOI:

[92]

Souvik Sengupta, Jordi Garcia, and Xavi Masip-Bruin. 2020. Collaborative learning-based schema for predicting resource usage and performance in F2C paradigm. In 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). IEEE Computer Society, 1–6. DOI:

Digital Library

[93]

Jeongin Seo and Hyeyoung Park. 2019. Object recognition in very low resolution images using deep collaborative learning. IEEE Access 7 (Sept.2019), 134071–134082. DOI:

[94]

Christopher J. Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, and George E. Dahl. 2019. Measuring the effects of data parallelism on neural network training. Journal of Machine Learning Research 20, 112 (July2019), 1–49.

[95]

Taihua Shao, Xiaoyan Kui, Pengfei Zhang, and Honghui Chen. 2019. Collaborative learning for answer selection in question answering. IEEE Access 7 (Jan.2019), 7337–7347. DOI:

[96]

Joseph Sifakis. 2019. Autonomous systems—An Architectural Characterization. Springer Nature Switzerland, 388–410. DOI:

[97]

David Silver, Satinder Singh, Doina Precup, and Richard S. Sutton. 2021. Reward is enough. Artificial Intelligence 299, 103535 (May2021), 1–13. DOI:

[98]

Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar. 2017. Federated multi-task learning. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17). Curran Associates Inc., Red Hook, NY, 4427–4437.

Digital Library

[99]

Mingcong Song, Kan Zhong, Jiaqi Zhang, Yang Hu, Yang Liu, Weigong Zhang, Jing Wang, and Tao Li. 2018. In-situ AI: Towards autonomous and incremental deep learning for IoT systems. In 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA). IEEE Computer Society, 92–103. DOI:

[100]

Haifeng Sun, Shiqi Li, F. Richard Yu, Qi Qi, Jingyu Wang, and Jianxin Liao. 2020. Toward communication-efficient federated learning in the Internet of Things with edge computing. IEEE Internet of Things Journal 7, 11 (May2020), 11053–11067. DOI:

[101]

Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, and H. Brendan McMahan. 2017. Distributed mean estimation with limited communication. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), Doina Precup and Yee Whye Teh (Eds.), Vol. 70. PMLR, 3329–3337.

[102]

Xiangyun Tang, Liehuang Zhu, Meng Shen, Jialiang Peng, Jiawen Kang, Dusit Niyato, and Ahmed A. Abd El-Latif. 2022. Secure and trusted collaborative learning based on blockchain for artificial intelligence of things. IEEE Wireless Communications 29, 3 (June2022), 14–22. DOI:

Digital Library

[103]

Tuan Nguyen Van, Nang Ho Xuan, and Nam Nguyen Duc. 2022. Design and development of multifunctional autonomous mobile disinfection robot against SARS-CoV-2 virus. International Journal of Mechanical Engineering and Robotics Research 11, 10 (Oct.2022), 718–723. DOI:

[104]

Joost Verbraeken, Matthijs Wolting, Jonathan Katzy, Jeroen Kloppenburg, Tim Verbelen, and Jan S. Rellermeyer. 2020. A survey on distributed machine learning. ACM Computing Survey 53, 30-2, Article 30 (March2020), 33 pages. DOI:

Digital Library

[105]

Deepak A. Vidhate and Parag Kulkarni. 2016. Performance enhancement of cooperative learning algorithms by improved decision making for context based application. In 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). IEEE Computer Society, 246–252. DOI:

[106]

Herna L. Viktor. 2000. Cooperating to learn: Knowledge discovery through intelligent learning agents. In Proceedings of the 4th International Conference on MultiAgent Systems. IEEE Computer Society, 453–454. DOI:

[107]

Hongbign Wang, Xin Chen, Qin Wu, Qi Yu, Xingguo Hu, Zibin Zheng, and Athman Bouguettaya. 2017. Integrating reinforcement learning with multi-agent techniques for adaptive service composition. ACM Transactions on Autonomous and Adaptive Systems 12, 2, Article 8 (May2017), 42 pages. DOI:

Digital Library

[108]

Qian Wang, Minxin Du, Xiuying Chen, Yanjiao Chen, Pan Zhou, Xiaofeng Chen, and Xinyi Huang. 2018. Privacy-preserving collaborative model learning: The case of word vector training. IEEE Transactions on Knowledge and Data Engineering 30, 12 (Dec.2018), 2381–2393. DOI:

Digital Library

[109]

Xin Wei, Jialin Zhao, Liang Zhou, and Yi Qian. 2020. Broad reinforcement learning for supporting fast autonomous IoT. IEEE Internet of Things Journal 7, 8 (Aug.2020), 7010–7020. DOI:

[110]

Peter Werkhoven, Leon Kester, and Mark Neerincx. 2018. Telling autonomous systems what to do. In Proceedings of the 36th European Conference on Cognitive Ergonomics (ECCE’18). ACM, New York, NY, Article 2, 8 pages. DOI:

Digital Library

[111]

Celimuge Wu, Zhi Liu, Fuqiang Liu, Tsutomu Yoshinaga, Yusheng Ji, and Jie Li. 2020. Collaborative learning of communication routes in edge-enabled multi-access vehicular environment. IEEE Transactions on Cognitive Communications and Networking 6, 4 (June2020), 1–11. DOI:

[112]

Maoqiang Wu, Dongdong Ye, Jiahao Ding, Yuanxiong Guo, Rong Yu, and Miao Pan. 2021. Incentivizing differentially private federated learning: A multi-dimensional contract approach. IEEE Internet of Things Journal 8, 13 (Jan.2021), 1–13. DOI:

[113]

Zhaohui Wu, Yuxin Mao, and Huajun Chen. 2009. Subontology-based resource management for web-based e-learning. IEEE Transactions on Knowledge and Data Engineering 21, 6 (July2009), 867–880. DOI:

Digital Library

[114]

Yutong Xie, Yong Xia, Jianpeng Zhang, Yang Song, Dagan Feng, Michael Fulham, and Weidong Cai. 2019. Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Transactions on Medical Imaging 38, 4 (Oct.2019), 991–1004. DOI:

[115]

Wenyuan Xu, Weiwei Fang, Yi Ding, Meixia Zou, and Naixue Xiong. 2021. Accelerating federated learning for IoT in big data analytics with pruning, quantization and selective updating. IEEE Access 9 (March2021), 38457–38466. DOI:

[116]

Dylan Yaga, Peter Mell, Mik Roby, and Karen Scarfone. 2018. Blockchain Technology Overview (1st ed.). Technical Report NISTIR 8202. National Institute of Standards and Technology. 66 pages. DOI:

[117]

Rong Yu and Peichun Li. 2021. Toward resource-efficient federated learning in mobile edge computing. IEEE Network 35, 1 (Feb.2021), 148–155. DOI:

Digital Library

[118]

Matei Zaharia, Reynold S. Xin, Patrick Wendell, Tathagata Das, Michael Armbrust, Ankur Dave, Xiangrui Meng, Josh Rosen, Shivaram Venkataraman, Michael J. Franklin, Ali Ghodsi, Joseph Gonzalez, Scott Shenker, and Ion Stoica. 2016. Apache spark: A unified engine for big data processing. Commun. ACM 59, 11 (Oct2016), 56–65. DOI:

Digital Library

[119]

Abderrahim Zannou, Abdelhak Boulaalam, and El Habib Nfaoui. 2021. Relevant node discovery and selection approach for the Internet of Things based on neural networks and ant colony optimization. Pervasive and Mobile Computing 70, 101311 (Jan.2021), 1–14. DOI:

[120]

Fuqiang Zhang, Yanrui Zhang, and Shilin Xu. 2022. Collaboration effectiveness-based complex operations allocation strategy towards to human–robot interaction. Autonomous Intelligent Systems 2, 20 (Aug.2022), 1–12. DOI:

[121]

Jie Zhang, Zhihao Qu, Chenxi Chen, Haozhao Wang, Yufeng Zhan, Baoliu Ye, and Song Guo. 2021. Edge learning: The enabling technology for distributed big data analytics in the edge. ACM Comput. Surv. 54, 7, Article 151 (July2021), 36 pages. DOI:

Digital Library

[122]

Qingchen Zhang, Laurence Tianruo Yang, Zhikui Chen, Peng Li, and Fanyu Bu. 2019. An adaptive dropout deep computation model for industrial IoT big data learning with crowdsourcing to cloud computing. IEEE Transactions on Industrial Informatics 15, 4 (April2019), 2330–2337. DOI:

[123]

Tiehua Zhang, Zhishu Shen, Jiong Jin, Xi Zheng, Atsushi Tagami, and Xianghui Cao. 2021. Achieving democracy in edge intelligence: A fog-based collaborative learning scheme. IEEE Internet of Things Journal 8, 4 (Feb.2021), 2751–2761. DOI:

[124]

Tao Zhang and Quanyan Zhu. 2018. Distributed privacy-preserving collaborative intrusion detection systems for VANETs. IEEE Transactions on Signal and Information Processing over Networks 4, 1 (Feb.2018), 148–161. DOI:

[125]

Wenyu Zhang, Xiumin Wang, Pan Zhou, Weiwei Wu, and Xinglin Zhang. 2021. Client selection for federated learning with non-IID data in mobile edge computing. IEEE Access 9 (Feb.2021), 24462–24474. DOI:

[126]

Bin Zhao, Kai Fan, Kan Yang, Zilong Wang, Hui Li, and Yintang Yang. 2021. Anonymous and privacy-preserving federated learning with industrial big data. IEEE Transactions on Industrial Informatics 17, 9 (Jan.2021), 1–10. DOI:

[127]

Lingchen Zhao, Qian Wang, Qin Zou, Yan Zhang, and Yanjiao Chen. 2020. Privacy-preserving collaborative deep learning with unreliable participants. IEEE Transactions on Information Forensics and Security 15 (Jan.2020), 1486–1500. DOI:

Digital Library

[128]

Yanqi Zhao, Xiaoyi Yang, Yong Yu, Baodong Qin, Xiaojiang Du, and Mohsen Guizani. 2022. Blockchain-based auditable privacy-preserving data classification for Internet of Things. IEEE Internet of Things Journal 9, 4 (Feb.2022), 2468–2484. DOI:

[129]

Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhi-Ming Ma, and Tie-Yan Liu. 2017. Asynchronous stochastic gradient descent with delay compensation. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research). Doina Precup and Yee Whye Teh (Eds.), Vol. 70. PMLR, Sydney, Australia, 4120–4129.

[130]

Wenting Zheng, Ryan Deng, Weikeng Chen, Raluca Ada Popa, Aurojit Panda, and Ion Stoica. 2021. Cerebro: A platform for multi-party cryptographic collaborative learning. In 30th USENIX Security Symposium (USENIX Security 21). USENIX Association, 2723–2740.

[131]

Wei Zhou, Dong Chen, Jun Yan, Zhaojian Li, Huilin Yin, and Wanchen Ge. 2022. Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic. Autonomous Intelligent Systems 2, 5 (March2022), 1–12. DOI:

[132]

Guangxu Zhu, Yong Wang, and Kaibin Huang. 2020. Broadband analog aggregation for low-latency federated edge learning. IEEE Transactions on Wireless Communications 19, 1 (Jan.2020), 491–506. DOI:

[133]

Hao Zhu, Yang Cao, Wei Wang, Tao Jiang, and Shi Jin. 2018. Deep reinforcement learning for mobile edge caching: Review, new features, and open issues. IEEE Network 32, 6 (Nov.2018), 50–57. DOI:

[134]

Hangyu Zhu and Yaochu Jin. 2020. Multi-objective evolutionary federated learning. IEEE Transactions on Neural Networks and Learning Systems 31, 4 (April2020), 1310–1322. DOI:

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Index Terms

A Survey on Collaborative Learning for Intelligent Autonomous Systems
1. Computing methodologies
  1. Artificial intelligence
  2. Distributed computing methodologies

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 4

April 2024

1026 pages

EISSN:1557-7341

DOI:10.1145/3613581

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 10 November 2023

Online AM: 28 September 2023

Accepted: 18 September 2023

Revised: 22 June 2023

Received: 07 January 2022

Published in CSUR Volume 56, Issue 4

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Cited By

Dritsas ETrigka M(2025)Exploring the Intersection of Machine Learning and Big Data: A SurveyMachine Learning and Knowledge Extraction10.3390/make70100137:1(13)Online publication date: 7-Feb-2025
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Tounsi MMahdi AAhmed MAzar ASmait DAhmed SZalzala AIbraheem I(2024)Hardware Implementation of a Deep Learning-based Autonomous System for Smart Homes using Field Programmable Gate Array TechnologyEngineering, Technology & Applied Science Research10.48084/etasr.837214:5(17203-17208)Online publication date: 9-Oct-2024
https://doi.org/10.48084/etasr.8372
Wang G(2024)Construction of the Index System for the Integration of Professional Education and Innovation Entrepreneurship Education in Applied UniversitiesInternational Journal of Information and Communication Technology Education10.4018/IJICTE.34998120:1(1-15)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/IJICTE.349981
Ali ZAlkhammash EHasan R(2024)State-of-the-Art Flocking Strategies for the Collective Motion of Multi-RobotsMachines10.3390/machines1210073912:10(739)Online publication date: 20-Oct-2024
https://doi.org/10.3390/machines12100739
Tantry RShenoy SAcharya SPrathibha K(2024)Artificial Intelligence Assisted Student States monitoring based on Enhancing Cognitive and Emotional Feedback Mechanism using Collaborative Learning Environments2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)10.1109/ACCAI61061.2024.10601800(1-5)Online publication date: 9-May-2024
https://doi.org/10.1109/ACCAI61061.2024.10601800
Lv YDing HWu HZhao YZhang L(2023)FedRDS: Federated Learning on Non-IID Data via Regularization and Data SharingApplied Sciences10.3390/app13231296213:23(12962)Online publication date: 4-Dec-2023
https://doi.org/10.3390/app132312962

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