Y. Supriya
Skip Abstract Section
Abstract
Federated Learning is a distributed, privacy-preserving machine learning model that is gaining more attention these days. Federated Learning has a vast number of applications in different fields. While being more popular, it also suffers some drawbacks like high communication costs, privacy concerns, and data management issues. In this survey, we define federated learning systems and analyse the system to ensure a smooth flow and to guide future research with the help of soft computing techniques. We undertake a complete review of aggregating federated learning systems with soft computing techniques. We also investigate the impacts of collaborating various nature-inspired techniques with federated learning to alleviate its flaws. Finally, this paper discusses the possible future developments of integrating federated learning and soft computing techniques.
- [1] . 2019. Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046 (2019).Google Scholar
- [2] . 2019. What is machine learning? A primer for the epidemiologist. American Journal of Epidemiology 188, 12 (2019), 2222–2239.Google Scholar
- [3] . 2019. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10, 2 (2019), 1–19.Google ScholarDigital Library
- [4] . 2020. A survey on security and privacy of federated learning. Future Generation Computer Systems (2020).Google Scholar
- [5] . 2021. Federated learning: Opportunities and challenges. arXiv preprint arXiv:2101.05428 (2021).Google Scholar
- [6] . 2016. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016).Google Scholar
- [7] . 2021. Federated learning for healthcare informatics. Journal of Healthcare Informatics Research 5, 1 (2021), 1–19.Google ScholarCross Ref
- [8] . 2020. Internet of things intrusion detection: Centralized, on-device, or federated learning? IEEE Network 34, 6 (2020), 310–317.Google ScholarDigital Library
- [9] . 2020. Federated learning with blockchain for autonomous vehicles: Analysis and design challenges. IEEE Transactions on Communications 68, 8 (2020), 4734–4746.Google ScholarCross Ref
- [10] . 2021. A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering (2021).Google Scholar
- [11] . 2019. A hybrid approach to privacy-preserving federated learning. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security. 1–11.Google ScholarDigital Library
- [12] . 2018. Differentially Private Federated Learning: A Client Level Perspective. (2018).
arxiv:cs.CR/1712.07557 Google Scholar - [13] . 2019. HybridAlpha: An efficient approach for privacy-preserving federated learning. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security. 13–23.Google ScholarDigital Library
- [14] . 2021. Evaluating the communication efficiency in federated learning algorithms. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). 552–557.
DOI: Google ScholarCross Ref - [15] . 2021. Communication-efficient federated learning. Proceedings of the National Academy of Sciences 118, 17 (2021).Google Scholar
- [16] . 2021. FedCPF: An efficient-communication federated learning approach for vehicular edge computing in 6G communication networks. IEEE Transactions on Intelligent Transportation Systems (2021), 1–14.Google Scholar
- [17] . 2019. CMFL: Mitigating communication overhead for federated learning. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). 954–964.
DOI: Google ScholarCross Ref - [18] . 2019. A survey on soft computing techniques and applications. Int. Res. J. Eng. Technol. 6, 4 (2019), 1258–1266.Google Scholar
- [19] . 2021. The application of soft computing models and empirical formulations for hydraulic structure scouring depth simulation: A comprehensive review, assessment and possible future research direction. Archives of Computational Methods in Engineering 28, 2 (2021), 423–447.Google Scholar
- [20] . 2018. A comprehensive review of soft computing techniques. International Journal of Applied Engineering Research 13, 11 (2018), 9881–9886.Google Scholar
- [21] . 2013. On soft computing techniques in various areas. Comput. Sci. Inf. Technol. 3, 59 (2013), 166.Google Scholar
- [22] . 2015. Survey in fuzzy logic: An introduction. Int. J. Sci. Res. Dev. 3, 6 (2015), 822–824.Google Scholar
- [23] . 2006. Fuzzy mode and its applications in survey research. In Proceedings of the 10th WSEAS International Conference on Applied Mathematics. 286–291.Google ScholarDigital Library
- [24] . 2011. Fuzzy logic applications for knowledge discovery: A survey. International Journal of Advancements in Computing Technology 3, 6 (2011).Google Scholar
- [25] . 1998. A survey of recent advances in fuzzy logic in telecommunications networks and new challenges. IEEE Transactions on Fuzzy Systems 6, 3 (1998), 443–447.
DOI: Google ScholarDigital Library - [26] . 2011. A survey on industrial applications of fuzzy control. Computers in Industry 62, 3 (2011), 213–226.Google ScholarDigital Library
- [27] . 2020. Nature-inspired Optimization Algorithms. Academic Press.Google Scholar
- [28] . 2014. A survey on application of bio-inspired algorithms. International Journal of Computer Science and Information Technologies 5, 1 (2014), 366–370.Google Scholar
- [29] . 2012. A survey of bio inspired optimization algorithms. International Journal of Soft Computing and Engineering 2, 2 (2012), 137–151.Google Scholar
- [30] . 2020. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37, 3 (2020), 50–60.Google ScholarCross Ref
- [31] . 2020. Threats to federated learning: A survey. arXiv preprint arXiv:2003.02133 (2020).Google Scholar
- [32] . 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. arXiv preprint arXiv:1510.00149 (2015).Google Scholar
- [33] . 2020. Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation. IEEE Transactions on Neural Networks and Learning Systems 31, 10 (
Oct. 2020), 4229–4238.Google ScholarCross Ref - [34] . 2019. Multi-objective Evolutionary Federated Learning. (2019).
arxiv:cs.LG/1812.07478 Google Scholar - [35] . 2015. Machine learning: Trends, perspectives, and prospects. Science 349, 6245 (2015), 255–260.Google ScholarCross Ref
- [36] . 2020. A review on deep learning for future smart cities. Internet Technology Letters n/a, n/a (2020), e187.Google Scholar
- [37] . 2019. A survey on distributed machine learning. arXiv preprint arXiv:1912.09789 (2019).Google Scholar
- [38] . 2020. Stability-based generalization analysis of distributed learning algorithms for big data. IEEE Transactions on Neural Networks and Learning Systems 31, 3 (2020), 801–812.Google ScholarCross Ref
- [39] . 2013. A survey of methods for distributed machine learning. Progress in Artificial Intelligence 2, 1 (2013), 1–11.Google ScholarCross Ref
- [40] . 2020. LAGC: Lazily aggregated gradient coding for straggler-tolerant and communication-efficient distributed learning. arXiv preprint arXiv:1905.09148 (2020).Google Scholar
- [41] . 2016. Strategies and principles of distributed machine learning on big data. Engineering 2, 2 (2016), 179–195.Google ScholarCross Ref
- [42] . 2020. Distributed learning algorithms for optimal data routing in IoT networks. IEEE Transactions on Signal and Information Processing over Networks 6 (2020), 179–195.Google ScholarCross Ref
- [43] . 2021. A survey on federated learning. Knowledge-Based Systems 216 (2021), 106775.Google ScholarCross Ref
- [44] . 2020. A learning-based incentive mechanism for federated learning. IEEE Internet of Things Journal 7, 7 (2020), 6360–6368.Google ScholarCross Ref
- [45] . 2022. Reliable customer analysis using federated learning and exploring deep-attention edge intelligence. Future Generation Computer Systems 127 (2022), 70–79.Google ScholarDigital Library
- [46] . 2021. A federated learning approach to frequent itemset mining in cyber-physical systems. Journal of Network and Systems Management 29, 4 (2021), 1–17.Google Scholar
- [47] . 2022. Hyper-graph attention based federated learning method for mental health detection. IEEE Journal of Biomedical and Health Informatics (2022).Google Scholar
- [48] . 2020. Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security 15 (2020), 3454–3469.Google ScholarDigital Library
- [49] . 2020. A survey on federated learning: The journey from centralized to distributed on-site learning and beyond. IEEE Internet of Things Journal 8, 7 (2020), 5476–5497.Google ScholarCross Ref
- [50] . 2022. Fusion of federated learning and industrial internet of things: A survey. Computer Networks (2022), 109048.Google Scholar
- [51] . 2019. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10, 2 (2019), 1–19.Google ScholarDigital Library
- [52] . 2019. Federated learning of out-of-vocabulary words. arXiv preprint arXiv:1903.10635 (2019).Google Scholar
- [53] . 2001. Soft Computing and Its Applications. World Scientific.Google ScholarDigital Library
- [54] . 2019. Concepts of Soft Computing. Springer.Google ScholarDigital Library
- [55] . 2002. Fusion of soft computing and hard computing in industrial applications: An overview. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 32, 2 (2002), 72–79.Google ScholarDigital Library
- [56] . 2016. An overview of soft computing. Procedia Computer Science 102 (2016), 34–38.Google ScholarDigital Library
- [57] . 2009. Fuzzy logic and science. In Views on Fuzzy Sets and Systems from Different Perspectives. Springer, 67–77.Google Scholar
- [58] . 2022. Nature inspired meta heuristic algorithms for optimization problems. Computing 104, 2 (2022), 251–269.Google ScholarDigital Library
- [59] . 2022. Nature-inspired metaheuristic algorithms for optimization. Meta-heuristic Optimization Techniques: Applications in Engineering 10 (2022), 1.Google Scholar
- [60] . 2015. Ant colony optimization based clustering methodology. Applied Soft Computing 28 (2015), 301–311.Google ScholarDigital Library
- [61] . 2016. Optimal power flow with SVC devices by using the artificial bee colony algorithm. Turkish Journal of Electrical Engineering & Computer Sciences 24, 1 (2016), 341–353.Google Scholar
- [62] . 1991. Genetic algorithms for real parameter optimization. In Foundations of Genetic Algorithms. Vol. 1. Elsevier, 205–218.Google Scholar
- [63] . 2014. Nature-inspired algorithms: State-of-art, problems and prospects. International Journal of Computer Applications 100, 14 (2014), 14–21.Google ScholarCross Ref
- [64] . 2017. Artificial neural networks. Cham: Springer International Publishing 39 (2017).Google Scholar
- [65] . 2018. Artificial neural networks. In Encyclopedia of Information Science and Technology, Fourth Edition. IGI Global, 120–131.Google Scholar
- [66] . 1989. Learning in artificial neural networks: A statistical perspective. Neural Computation 1, 4 (1989), 425–464.Google ScholarDigital Library
- [67] . 2000. Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods 43, 1 (2000), 3–31.
DOI: Neural Computing in Microbiology. Google ScholarCross Ref - [68] . 2018. State-of-the-art in artificial neural network applications: A survey. Heliyon 4, 11 (2018), e00938.
DOI: Google ScholarCross Ref - [69] . 2018. Development and application of artificial neural network. Wireless Personal Communications 102, 2 (2018), 1645–1656.Google ScholarDigital Library
- [70] . 2016. An introduction to artificial neural network. Int. J. Adv. Res. Innov. Ideas Educ. 1 (2016), 27–30.Google Scholar
- [71] . 2011. Evolutionary computation. In Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence. Springer, 15–36.Google Scholar
- [72] . 2015. From evolutionary computation to the evolution of things. Nature 521, 7553 (2015), 476–482.Google ScholarCross Ref
- [73] . 2016. Evolutionary computation: A unified approach. In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. 185–199.Google Scholar
- [74] . 2012. The Evolutionary Strategies that Shape Ecosystems. John Wiley & Sons.Google Scholar
- [75] . 2005. Genetic programming. In Search Methodologies. Springer, 127–164.Google Scholar
- [76] . 2021. Evolutionary computation for solving search-based data analytics problems. Artificial Intelligence Review 54, 2 (2021), 1321–1348.Google ScholarDigital Library
- [77] . 2012. Advances in Evolutionary Computing: Theory and Applications. Springer Science & Business Media.Google Scholar
- [78] . 2021. Evolutionary computation in social propagation over complex networks: A survey. International Journal of Automation and Computing (2021), 1–18.Google Scholar
- [79] . 2021. Multiple-criteria decision-making sorting methods: A survey. Expert Systems with Applications 183 (2021), 115368.Google ScholarDigital Library
- [80] . 2009. Multicriteria decision making (MCDM): A framework for research and applications. Computational Intelligence Magazine, IEEE 4 (
09 2009), 48–61.DOI: Google ScholarDigital Library - [81] . 2014. State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy 20, 1 (2014), 165–179.Google ScholarCross Ref
- [82] . 2013. A survey on multi criteria decision making methods and its applications. American Journal of Information Systems 1, 1 (2013), 31–43.Google Scholar
- [83] . 2021. Risk quantification in cold chain management: A federated learning-enabled multi-criteria decision-making methodology. Industrial Management & Data Systems (2021).Google Scholar
- [84] . 2021. Combining multi-criteria decision making (MCDM) methods with building information modelling (BIM): A review. Automation in Construction 121 (2021), 103451.Google ScholarCross Ref
- [85] . 2021. Applications of fuzzy multiple criteria decision making methods in civil engineering: A state-of-the-art survey. Journal of Civil Engineering and Management 27, 6 (2021), 358–371.Google Scholar
- [86] . 2021. Federated learning for cybersecurity: Concepts, challenges, and future directions. IEEE Transactions on Industrial Informatics 18, 5 (2021), 3501–3509.Google Scholar
- [87] . 2022. Federated learning enabled digital twins for smart cities: Concepts, recent advances, and future directions. Sustainable Cities and Society 79 (2022), 103663.Google ScholarCross Ref
- [88] . 2022. Integration of blockchain technology and federated learning in vehicular (IoT) networks: A comprehensive survey. Sensors 22, 12 (2022), 4394.Google ScholarCross Ref
- [89] . 2021. From federated learning to federated neural architecture search: A survey. Complex & Intelligent Systems 7, 2 (2021), 639–657.Google ScholarCross Ref
- [90] . 2021. Real-time federated evolutionary neural architecture search. IEEE Transactions on Evolutionary Computation (2021).Google Scholar
- [91] . 2020. Sliding differential evolution scheduling for federated learning in bandwidth-limited networks. IEEE Communications Letters 25, 2 (2020), 503–507.Google Scholar
- [92] . 2020. Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services. (2020).
arxiv:cs.LG/2009.02560 Google Scholar - [93] . 2021. Genetic CFL: Optimization of hyper-parameters in clustered federated learning. arXiv preprint arXiv:2107.07233 (2021).Google Scholar
- [94] . 2021. FedGraphNN: A federated learning benchmark system for graph neural networks. In ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML).Google Scholar
- [95] . 2021. An efficient attribute reduction and fuzzy logic classifier for heart disease and diabetes prediction. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science) 14, 1 (2021), 158–165.Google Scholar
- [96] . 2021. Fuzzy consensus with federated learning method in medical systems. IEEE Access 9 (2021), 150383–150392.Google ScholarCross Ref
- [97] . 2022. Fuzzy clustered federated learning algorithm for solar power generation forecasting. IEEE Transactions on Emerging Topics in Computing (2022).Google Scholar
- [98] . 2021. Federated learning and autonomous UAVs for hazardous zone detection and AQI prediction in IoT environment. IEEE Internet of Things Journal 8, 20 (2021), 15456–15467.Google ScholarCross Ref
- [99] . 2021. FedPSO: Federated learning using particle swarm optimization to reduce communication costs. Sensors 21, 2 (2021), 600.Google ScholarCross Ref
- [100] . 2021. A federated data-driven evolutionary algorithm for expensive multi-/ many-objective optimization. Complex & Intelligent Systems 7, 6 (2021), 3093–3109.Google ScholarCross Ref
- [101] . 2016. Federated optimization: Distributed machine learning for on-device intelligence. ArXiv abs/1610.02527 (2016).Google Scholar
- [102] . 2021. CBFL: A communication-efficient federated learning framework from data redundancy perspective. IEEE Systems Journal (2021).Google Scholar
- [103] . 2021. FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search. (2021).
arxiv:cs.LG/2104.04141 Google Scholar - [104] . 2021. Neural network quantization in federated learning at the edge. Information Sciences 575 (2021), 417–436.Google ScholarDigital Library
- [105] . 2019. An end-to-end encrypted neural network for gradient updates transmission in federated learning. arXiv preprint arXiv:1908.08340 (2019).Google Scholar
- [106] . 2022. Federated Deep Learning in Electricity Forecasting: An MCDM Approach. (2022).
arxiv:math.OC/2111.13834 Google Scholar - [107] . 2021. Resiliency metrics for monitoring and analysis of cyber-power distribution system with IoTs. IEEE Access (2021).Google Scholar
- [108] . 2022. Data freshness optimization under CAA in the UAV-aided MECN: A potential game perspective. IEEE Transactions on Intelligent Transportation Systems (2022).Google Scholar
- [109] . 2022. Incentive mechanisms for smart grid: State of the art, challenges, open issues, future directions. Big Data and Cognitive Computing 6, 2 (2022), 47.Google ScholarCross Ref
- [110] . 2022. A dynamic incentive and reputation mechanism for energy-efficient federated learning in 6G. Digital Communications and Networks (2022).Google Scholar
- [111] . 2022. Equality is not equity: Proportional fairness in federated learning. arXiv preprint arXiv:2202.01666 (2022).Google Scholar
- [112] . 2022. Fair and efficient contribution valuation for vertical federated learning. arXiv preprint arXiv:2201.02658 (2022).Google Scholar
- [113] . 2022. A coalition formation game approach for personalized federated learning. arXiv preprint arXiv:2202.02502 (2022).Google Scholar
- [114] . 2021. A comprehensive survey of incentive mechanism for federated learning. arXiv preprint arXiv:2106.15406 (2021).Google Scholar
- [115] . 2021. Federated learning versus classical machine learning: A convergence comparison. Authorea Preprints (2021).Google Scholar
Index Terms
- A Survey on Soft Computing Techniques for Federated Learning- Applications, Challenges and Future Directions
Recommendations
Blockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions
Federated learning is a privacy-preserving machine learning technique that trains models across multiple devices holding local data samples without exchanging them. There are many challenging issues in federated learning, such as coordinating participants’...
Challenges and future directions of secure federated learning: a survey
AbstractFederated learning came into being with the increasing concern of privacy security, as people’s sensitive information is being exposed under the era of big data. It is an algorithm that does not collect users’ raw data, but aggregates model ...
A review of federated learning: taxonomy, privacy and future directions
AbstractThe data generated and stored in mobile devices owned by individuals as well as in various organizations contains a large amount of valuable and important information that can be used to improve service quality, user experience, and satisfaction. ...
Comments