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.
- [1] . 2022. Federated learning in edge computing: A systematic survey. Sensors 22, 2 (
Jan. 2022), 1–45.DOI: Google ScholarCross Ref - [2] . 2022. Apache Spark. Retrieved from https://spark.apache.org/Google Scholar
- [3] . 2017. Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine 34, 6 (
Nov. 2017), 26–38.DOI: Google ScholarCross Ref - [4] . 2016. Architecture challenges for intelligent autonomous machines. In Intelligent Autonomous Systems 13 (Advances in Intelligent Systems and Computing (AISC)). , , , and (Eds.), Vol. 302. Springer International Publishing, Cham, 1669–1681.
DOI: Google ScholarCross Ref - [5] . 2021. Deep learning for AI. Commun. ACM 64, 7 (
June 2021), 58–65.DOI: Google ScholarDigital Library - [6] . 2020. Autonomous IoT device management systems: Structured review and generalized cognitive model. IEEE Internet of Things Journal 8, 6 (
Nov. 2020), 1–16.DOI: Google ScholarCross Ref - [7] . 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: Google ScholarDigital Library - [8] . 2019. Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer. CoRR abs/1912.11279, 1–16. http://arxiv.org/abs/1912.11279Google Scholar
- [9] . 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: Google ScholarCross Ref - [10] . 2020. Label-less learning for emotion cognition. IEEE Transactions on Neural Networks and Learning Systems 31, 7 (
July 2020), 2430–2440.DOI: Google ScholarCross Ref - [11] . 2020. Wireless communications for collaborative federated learning. IEEE Communications Magazine 58, 12 (
Dec. 2020), 48–54.DOI: Google ScholarCross Ref - [12] . 2021. Privacy-preserving collaborative learning for multiarmed bandits in IoT. IEEE Internet of Things Journal 8, 5 (
March 2021), 3276–3286.DOI: Google ScholarCross Ref - [13] . 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: Google ScholarCross Ref - [14] . 2021. Zero knowledge clustering based adversarial mitigation in heterogeneous federated learning. IEEE Transactions on Network Science and Engineering 8, 2 (
June 2021), 1070–1083.DOI: Google ScholarCross Ref - [15] . 2020. Semisupervised distributed learning with non-IID data for AIoT service platform. IEEE Internet of Things Journal 7, 10 (
May 2020), 9266–9277.DOI: Google ScholarCross Ref - [16] . 2021. Variational federated multi-task learning. CoRR abs/1906.06268, 1–12. https://arxiv.org/abs/1906.06268Google Scholar
- [17] . 2017. How people explain action (and autonomous intelligent systems should too). In 2017 AAAI Fall Symposium Series. The AAAI Press, 1–8. Google Scholar
- [18] . 2020. Verifiable self-aware agent-based autonomous systems. Proc. IEEE 108, 7 (
July 2020), 1011–1026.DOI: Google ScholarCross Ref - [19] . 2022. Multi-agent reinforcement learning for autonomous vehicles: A survey. Autonomous Intelligent Systems 2, 27 (
Nov. 2022), 1–12. showISSN2730-616XDOI: Google ScholarCross Ref - [20] , , and (Eds.). 2020. Deep Reinforcement Learning—Fundamentals, Research and Applications. Springer Nature Singapore, Gateway East, Singapore. 526 pages. Google ScholarCross Ref
- [21] . 2021. An algorithm to minimize energy consumption and elapsed time for IoT workloads in a hybrid architecture. Sensors 21, 9 (
April 2021), 1–20.DOI: Google ScholarCross Ref - [22] . 2020. Data processing model to perform big data analytics in hybrid infrastructures. IEEE Access 8 (
Sept. 2020), 170281–170294.DOI: Google ScholarCross Ref - [23] . 2018. Enabling strategies for big data analytics in hybrid infrastructures. HPCS—-International Conference on High Performance Computing and Simulation, 869–876.
DOI: Google ScholarCross Ref - [24] . 2020. Federated learning for vehicular internet of things: Recent advances and open issues. IEEE Open Journal of the Computer Society 1 (
May 2020), 45–61.DOI: Google ScholarCross Ref - [25] . 2019. Semi-cyclic stochastic gradient descent. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research). and (Eds.), Vol. 97. PMLR, 1764–1773.Google Scholar
- [26] . 2021. Sustainability of healthcare data analysis IoT-based systems using deep federated learning. IEEE Internet of Things Journal 9, 10 (2021), 1–9.Google Scholar
- [27] . 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: Google ScholarCross Ref - [28] . 2020. Reinforcement R-learning model for time scheduling of on-demand fog placement. The Journal of Supercomputing 76, 1 (2020), 388–410.
DOI: Google ScholarDigital Library - [29] . 2021. Blockchain-based asynchronous federated learning for Internet of Things. IEEE Trans. Comput. 71, 5 (
April 2021), 1–12.DOI: Google ScholarCross Ref - [30] . 2018. Anytime Stochastic Gradient Descent: A Time to Hear from all the Workers. Cornell University, ArXiv, 1–10. https://arxiv.org/abs/1810.02976Google Scholar
- [31] . 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: Google ScholarCross Ref - [32] . 2020. ISABELA—A socially-aware human-in-the-loop advisor system. Online Social Networks and Media 16 (
March 2020), 100060.DOI: Google ScholarCross Ref - [33] . 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: Google ScholarCross Ref - [34] . 2019. iRBP-Motif-PSSM: Identification of RNA-binding proteins based on collaborative learning. IEEE Access 7 (
Nov. 2019), 168956–168962.DOI: Google ScholarCross Ref - [35] . 2019. Data offloading in IoT environments: Modeling, analysis, and verification. EURASIP Journal on Wireless Communications and Networking 2019, 59 (
March 2019), 1–23.DOI: Google ScholarCross Ref - [36] . 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. , , , , , and (Eds.), Vol. 12571. Springer International Publishing, Cham, 101–109.
DOI: Google ScholarDigital Library - [37] . 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: Google ScholarCross Ref - [38] . 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: Google ScholarCross Ref - [39] . 2020. Taming Momentum in a Distributed Asynchronous Environment. Cornell University, 1–18. https://arxiv.org/abs/1907.11612Google Scholar
- [40] . 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: Google ScholarCross Ref - [41] . 2021. Liquid time-constant networks. In Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21). 7657–7666.Google ScholarCross Ref
- [42] . 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. Google ScholarCross Ref
- [43] . 2019. Deep collaborative learning with application to the study of multimodal brain development. IEEE Transactions on Biomedical Engineering 66, 12 (
March 2019), 3346–3359.DOI: Google ScholarCross Ref - [44] . 2020. Blockchain-based federated learning for intelligent control in heavy haul railway. IEEE Access 8 (
Sept. 2020), 176830–176839.DOI: Google ScholarCross Ref - [45] . 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: Google ScholarCross Ref - [46] . 2019. Efficient privacy-preserving machine learning in hierarchical distributed system. IEEE Transactions on Network Science and Engineering 6, 4 (
Dec. 2019), 599–612.DOI: Google ScholarCross Ref - [47] . 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: Google ScholarDigital Library - [48] . 2020. Computational intelligence for safety assurance of cooperative systems of systems. Computer 53, 12 (
Nov. 2020), 24–34.DOI: Google ScholarDigital Library - [49] . 2019. Computing with high-dimensional vectors. IEEE Design Test 36, 3 (
Dec. 2019), 7–14.DOI: Google ScholarCross Ref - [50] . 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: Google ScholarDigital Library - [51] . 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: Google ScholarDigital Library - [52] . 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: Google ScholarDigital Library - [53] . 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: Google ScholarCross Ref - [54] . 2020. On-the-fly closed-loop materials discovery via Bayesian active learning. Nature Communications Volume 11, 5966(2020) (
Nov. 2020), 1–11.DOI: Google ScholarCross Ref - [55] . 2015. Multirobot cooperative learning for predator avoidance. IEEE Transactions on Control Systems Technology 23, 1 (
April 2015), 52–63.DOI: Google ScholarCross Ref - [56] . 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: Google ScholarDigital Library - [57] . 2020. Deep reinforcement learning for autonomous Internet of Things: Model, applications and challenges. IEEE Communications Surveys Tutorials 22, 3 (
April 2020), 1722–1760.DOI: Google ScholarCross Ref - [58] . 2020. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37, 3 (
May 2020), 50–60.DOI: Google ScholarCross Ref - [59] . 2020. Federated optimization in heterogeneous networks. In Proceedings of Machine Learning and Systems 2020, MLSys 2020, , , and (Eds.), Vol. 2. mlsys.org, Austin, TX, 429–450.Google Scholar
- [60] . 2018. Human action recognition based on selected spatio-temporal features via bidirectional LSTM. IEEE Access 6 (
Aug. 2018), 44211–44220.DOI: Google ScholarCross Ref - [61] . 2018. A collaborative learning framework for estimating many individualized regression models in a heterogeneous population. IEEE Transactions on Reliability 67, 1 (
March 2018), 328–341.DOI: Google ScholarCross Ref - [62] . 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: Google ScholarDigital Library - [63] . 2018. Incremental learning through graceful degradations in autonomous systems. In 2018 IEEE International Conference on Cognitive Computing (ICCC). IEEE Computer Society, 25–32.
DOI: Google ScholarCross Ref - [64] . 2022. Blockchain assisted secure data sharing model for Internet of Things based smart industries. IEEE Transactions on Reliability 71, 1 (
March 2022), 348–358.DOI: Google ScholarCross Ref - [65] . 2022. Performance evaluation analysis of spark streaming backpressure for data-intensive pipelines. Sensors 22, 13 (2022), 4756(1–28).
DOI: Google ScholarCross Ref - [66] . 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: Google ScholarCross Ref - [67] . 2021. Evolution and revolution: Personality research for the coming world of robots, artificial intelligence, and autonomous systems. Personality and Individual Differences 169, 109969 (
March 2021), 1–11.DOI: Google ScholarCross Ref - [68] . 2016. Resilient autonomous systems: Challenges and solutions. In 2016 Resilience Week (RWS). IEEE Computer Society, 208–213.
DOI: Google ScholarCross Ref - [69] . 2019. Exploiting unintended feature leakage in collaborative learning. In 2019 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, 691–706.
DOI: Google ScholarCross Ref - [70] . 2020. Multi-colony collaborative ant optimization algorithm based on cooperative game mechanism. IEEE Access 8 (
Sept. 2020), 154153–154165.DOI: Google ScholarCross Ref - [71] . 2018. Enabling cognitive smart cities using big data and machine learning: Approaches and challenges. IEEE Communications Magazine 56, 2 (
Feb. 2018), 94–101.DOI: Google ScholarDigital Library - [72] . 2022. World Robotics 2022 – Industrial Robots.
Technical Report . Frankfurt, Germany. 98 pages. Google Scholar - [73] . 2019. Trust in autonomous systems-iTrust lab: Future directions for analysis of trust with autonomous systems. IEEE Systems, Man, and Cybernetics Magazine 5, 3 (
July 2019), 52–59.DOI: Google ScholarCross Ref - [74] . 2018. A Practical Introduction to Human-in-the-Loop Cyber-Physical Systems. John Wiley & Sons. 289 pages. Google ScholarDigital Library
- [75] . 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: Google ScholarDigital Library - [76] . 2020. Anytime computation and control for autonomous systems. IEEE Transactions on Control Systems Technology 29, 2 (
Mar 2020), 1–12.DOI: Google ScholarCross Ref - [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/1610.05755Google Scholar
- [78] . 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: Google ScholarDigital Library - [79] . 2016. General data protection regulation. Official Journal of the European Union 59 (
May 2016), 1–149. Retrieved from https://gdpr-info.eu/Google Scholar - [80] . 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: Google ScholarCross Ref - [81] . 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: Google ScholarCross Ref - [82] . 2022. Securing federated learning with blockchain: A systematic literature review. Artificial Intelligence Review 56, 5 (
Sept. 2022), 1–35.DOI: Google ScholarDigital Library - [83] . 2018. Cross-domain collaborative learning via discriminative nonparametric Bayesian model. IEEE Transactions on Multimedia 20, 8 (
Dec. 2018), 2086–2099.DOI: Google ScholarCross Ref - [84] . 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: Google ScholarCross Ref - [85] . 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: Google ScholarCross Ref - [86] . 2022. RAPIDS—Accelerator for Apache Spark Leverages GPUs. Retrieved from https://nvidia.github.io/spark-rapids/Google Scholar
- [87] . 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/Google Scholar
- [88] . 2021. Artificial Intelligence, A Modern Approach (4th ed.). Pearson Education Limited. 1066 pages. Google Scholar
- [89] . 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: Google ScholarCross Ref - [90] . 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: Google ScholarDigital Library - [91] . 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: Google ScholarCross Ref - [92] . 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: Google ScholarDigital Library - [93] . 2019. Object recognition in very low resolution images using deep collaborative learning. IEEE Access 7 (
Sept. 2019), 134071–134082.DOI: Google ScholarCross Ref - [94] . 2019. Measuring the effects of data parallelism on neural network training. Journal of Machine Learning Research 20, 112 (
July 2019), 1–49.Google Scholar - [95] . 2019. Collaborative learning for answer selection in question answering. IEEE Access 7 (
Jan. 2019), 7337–7347.DOI: Google ScholarCross Ref - [96] . 2019. Autonomous systems—An Architectural Characterization. Springer Nature Switzerland, 388–410.
DOI: Google ScholarCross Ref - [97] . 2021. Reward is enough. Artificial Intelligence 299, 103535 (
May 2021), 1–13.DOI: Google ScholarCross Ref - [98] . 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. Google ScholarDigital Library
- [99] . 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: Google ScholarCross Ref - [100] . 2020. Toward communication-efficient federated learning in the Internet of Things with edge computing. IEEE Internet of Things Journal 7, 11 (
May 2020), 11053–11067.DOI: Google ScholarCross Ref - [101] . 2017. Distributed mean estimation with limited communication. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), and (Eds.), Vol. 70. PMLR, 3329–3337.Google Scholar
- [102] . 2022. Secure and trusted collaborative learning based on blockchain for artificial intelligence of things. IEEE Wireless Communications 29, 3 (
June 2022), 14–22.DOI: Google ScholarDigital Library - [103] . 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: Google ScholarCross Ref - [104] . 2020. A survey on distributed machine learning. ACM Computing Survey 53, 30-2, Article
30 (March 2020), 33 pages.DOI: Google ScholarDigital Library - [105] . 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: Google ScholarCross Ref - [106] . 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: Google ScholarCross Ref - [107] . 2017. Integrating reinforcement learning with multi-agent techniques for adaptive service composition. ACM Transactions on Autonomous and Adaptive Systems 12, 2, Article
8 (May 2017), 42 pages.DOI: Google ScholarDigital Library - [108] . 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: Google ScholarDigital Library - [109] . 2020. Broad reinforcement learning for supporting fast autonomous IoT. IEEE Internet of Things Journal 7, 8 (
Aug. 2020), 7010–7020.DOI: Google ScholarCross Ref - [110] . 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: Google ScholarDigital Library - [111] . 2020. Collaborative learning of communication routes in edge-enabled multi-access vehicular environment. IEEE Transactions on Cognitive Communications and Networking 6, 4 (
June 2020), 1–11.DOI: Google ScholarCross Ref - [112] . 2021. Incentivizing differentially private federated learning: A multi-dimensional contract approach. IEEE Internet of Things Journal 8, 13 (
Jan. 2021), 1–13.DOI: Google ScholarCross Ref - [113] . 2009. Subontology-based resource management for web-based e-learning. IEEE Transactions on Knowledge and Data Engineering 21, 6 (
July 2009), 867–880.DOI: Google ScholarDigital Library - [114] . 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: Google ScholarCross Ref - [115] . 2021. Accelerating federated learning for IoT in big data analytics with pruning, quantization and selective updating. IEEE Access 9 (
March 2021), 38457–38466.DOI: Google ScholarCross Ref - [116] . 2018. Blockchain Technology Overview (1st ed.).
Technical Report NISTIR 8202. National Institute of Standards and Technology. 66 pages.DOI: Google ScholarCross Ref - [117] . 2021. Toward resource-efficient federated learning in mobile edge computing. IEEE Network 35, 1 (
Feb. 2021), 148–155.DOI: Google ScholarDigital Library - [118] . 2016. Apache spark: A unified engine for big data processing. Commun. ACM 59, 11 (
Oct 2016), 56–65.DOI: Google ScholarDigital Library - [119] . 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: Google ScholarCross Ref - [120] . 2022. Collaboration effectiveness-based complex operations allocation strategy towards to human–robot interaction. Autonomous Intelligent Systems 2, 20 (
Aug. 2022), 1–12.DOI: Google ScholarCross Ref - [121] . 2021. Edge learning: The enabling technology for distributed big data analytics in the edge. ACM Comput. Surv. 54, 7, Article
151 (July 2021), 36 pages.DOI: Google ScholarDigital Library - [122] . 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 (
April 2019), 2330–2337.DOI: Google ScholarCross Ref - [123] . 2021. Achieving democracy in edge intelligence: A fog-based collaborative learning scheme. IEEE Internet of Things Journal 8, 4 (
Feb. 2021), 2751–2761.DOI: Google ScholarCross Ref - [124] . 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: Google ScholarCross Ref - [125] . 2021. Client selection for federated learning with non-IID data in mobile edge computing. IEEE Access 9 (
Feb. 2021), 24462–24474.DOI: Google ScholarCross Ref - [126] . 2021. Anonymous and privacy-preserving federated learning with industrial big data. IEEE Transactions on Industrial Informatics 17, 9 (
Jan. 2021), 1–10.DOI: Google ScholarCross Ref - [127] . 2020. Privacy-preserving collaborative deep learning with unreliable participants. IEEE Transactions on Information Forensics and Security 15 (
Jan. 2020), 1486–1500.DOI: Google ScholarDigital Library - [128] . 2022. Blockchain-based auditable privacy-preserving data classification for Internet of Things. IEEE Internet of Things Journal 9, 4 (
Feb. 2022), 2468–2484.DOI: Google ScholarCross Ref - [129] . 2017. Asynchronous stochastic gradient descent with delay compensation. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research). and (Eds.), Vol. 70. PMLR, Sydney, Australia, 4120–4129.Google Scholar
- [130] . 2021. Cerebro: A platform for multi-party cryptographic collaborative learning. In 30th USENIX Security Symposium (USENIX Security 21). USENIX Association, 2723–2740.Google Scholar
- [131] . 2022. Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic. Autonomous Intelligent Systems 2, 5 (
March 2022), 1–12.DOI: Google ScholarCross Ref - [132] . 2020. Broadband analog aggregation for low-latency federated edge learning. IEEE Transactions on Wireless Communications 19, 1 (
Jan. 2020), 491–506.DOI: Google ScholarCross Ref - [133] . 2018. Deep reinforcement learning for mobile edge caching: Review, new features, and open issues. IEEE Network 32, 6 (
Nov. 2018), 50–57.DOI: Google ScholarCross Ref - [134] . 2020. Multi-objective evolutionary federated learning. IEEE Transactions on Neural Networks and Learning Systems 31, 4 (
April 2020), 1310–1322.DOI: Google ScholarCross Ref
Index Terms
- A Survey on Collaborative Learning for Intelligent Autonomous Systems
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