Federated Learning in Mobile Edge Networks: A Comprehensive Survey
Open accessAbstract
In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.
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Federated learning is a distributed machine learning framework that can train a unified deep learning model across multiple decentralized nodes, where each node holds its own local data samples for training, and those samples never need to be exchanged with any other nodes.
“Federated learning is a distributed machine learning framework that can train a unified deep learning model across multiple decentralized nodes, where each node holds its own local data samples for training, and those samples never need to be exchanged with any other nodes [94].”
From Machine Unlearning: A Survey (2024) · cites this paper as [94]
FL technique assures user privacy and data security because it does not access raw data.
“This technique assures user privacy and data security because it does not access raw data [21].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [21]
Global convergence is still guaranteed in CMFL.
“Global convergence is still guaranteed.”
From Challenges, Applications and Design Aspects of Federated Learning: A Survey (2021) · cites this paper as [29]
Authors in [29] have come up with CMFL algorithm, which reduces the cost of communication by only uploading updates of the local model that are relevant.
“Authors in [29] have come up with the Communication-Mitigated Federated Learning (CMFL) algorithm, which reduces the cost of communication by only uploading updates of the local model that are relevant.”
From Challenges, Applications and Design Aspects of Federated Learning: A Survey (2021) · cites this paper as [29]
By doing so, FedProx limits the impact of non-independent and identically distributed (non-IID) device data on the global learning model.
“By doing so, FedProx limits the impact of non-independent and identically distributed (non-IID) device data on the global learning model.”
From Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism (2020) · cites this paper as [7]
FedAvg does not guarantee theoretical convergence, while FedProx shows theoretical convergence.
“FedAvg does not guarantee theoretical convergence, while FedProx shows theoretical convergence.”
From Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism (2020) · cites this paper as [7]
The edges frequently collect and share data in a non-i.i.d. manner across the network.
“The edges frequently collect and share data in a non-i.i.d.manner across the network [12,25,139,184].”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [12]
Privacy is often a major concern in FL applications in comparison to learning in the centralized data in data centers.
“Privacy is often a major concern in FL applications in comparison to learning in the centralized data in data centers [12].”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [12]
Homomorphic Encryption protecting measures have been initially designed for privacy purposes and do not prevent FL from being susceptible to HE attacks.
“However, these protecting measures have been initially designed for privacy purposes and do not prevent FL from being susceptible to HE attacks [28], [29].”
From Vulnerabilities in federated learning (2021) · cites this paper as [29]
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Federated Learning for Healthcare Informatics
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“, personalization techniques [59], semi-supervised learning algorithms [49], threat models [68], and mobile edge networks [66].”
BackgroundFederated Learning for Internet of Things: A Comprehensive Survey
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“Several possible selection factors for this selection can be channel conditions and the importance of local updates of each IoT device [17].”
BackgroundMethodInfluentialConvergence of Edge Computing and Deep Learning: A Comprehensive Survey
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“To the best of our knowledge, existing articles that are most related to our work include [18]–[21].”
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Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications
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Federated learning meets blockchain in edge computing: Opportunities and challenges
Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3072611 · Cited by 661 · see citing works
Self-citationYin, X., Zhu, Y., & Hu, J. (2022) · ACM Computing Surveys · doi:10.1145/3460427 · Cited by 638 · see citing works
Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
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“In this sense, in 2020, Lim et al. [23] reviewed the use of CFL in mobile networks using different end-user applications, while in 2021, researchers such as Nguyen et al. [24] and Khan et al. [25] examined the application of FL in more complex scenarios involving different heterogeneous IoT devices.”
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., & Liu, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3017377 · Cited by 613 · see citing works
Self-citationFederated learning review: Fundamentals, enabling technologies, and future applications
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Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
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Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges
Nguyen, V.-L., Lin, P.-C., Cheng, B.-C., Hwang, R.-H., & Lin, Y.-D. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3108618 · Cited by 515 · see citing works
Local Differential Privacy-Based Federated Learning for Internet of Things
Zhao, Y., Zhao, J., Yang, M., Wang, T., Wang, N., Lyu, L., Niyato, D., & Lam, K.-Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3037194 · Cited by 456 · see citing works
Self-citationFederated Learning for intrusion detection system: Concepts, challenges and future directions
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Blockchain for 5G and beyond networks: A state of the art survey
Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102693 · Cited by 412 · see citing works
Federated learning for the Internet of Things: Applications, challenges, and opportunities
Zhang, T., Gao, L., He, C., Zhang, M., Krishnamachari, B., & Avestimehr, A. S. (2022) · IEEE Internet of Things Magazine · doi:10.1109/iotm.004.2100182 · Cited by 412 · see citing works
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399 · see citing works
FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring
Wu, Q., Chen, X., Zhou, Z., & Zhang, J. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3045266 · Cited by 392 · see citing works
Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
Shi, W., Zhou, S., Niu, Z., Jiang, M., & Geng, L. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3025446 · Cited by 381 · see citing works
Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge
Qayyum, A., Ahmad, K., Ahsan, M. A., Al-Fuqaha, A., & Qadir, J. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3206407 · Cited by 345 · see citing works
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342 · see citing works
Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329061 · Cited by 315 · see citing works
Blockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions
Zhu, J., Cao, J., Saxena, D., Jiang, S., & Ferradi, H. (2023) · ACM Computing Surveys · doi:10.1145/3570953 · Cited by 309 · see citing works
Ramu, S. P., Boopalan, P., Pham, Q.-V., Maddikunta, P. K. R., Huynh-The, T., Alazab, M., Nguyen, T. T., & Gadekallu, T. R. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103663 · Cited by 301 · see citing works
Deep learning for edge computing applications: A State-of-the-Art survey
Wang, F., Zhang, M., Wang, X., Ma, X., & Liu, J. (2020) · IEEE Access · doi:10.1109/access.2020.2982411 · Cited by 259 · see citing works
Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems
Brik, B., Ksentini, A., & Bouaziz, M. (2020) · IEEE Access · doi:10.1109/access.2020.2981430 · Cited by 258 · see citing works
Vulnerabilities in federated learning
Bouacida, N., & Mohapatra, P. (2021) · IEEE Access · doi:10.1109/access.2021.3075203 · Cited by 248 · see citing works
Poisoning attacks in federated learning: A survey
Xia, G., Chen, J., Yu, C., & Ma, J. (2023) · IEEE Access · doi:10.1109/access.2023.3238823 · Cited by 212 · see citing works
Design and Analysis of Uplink and Downlink Communications for Federated Learning
Zheng, S., Shen, C., & Chen, X. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3041388 · Cited by 191 · see citing works
Bashir, A. K., Victor, N., Bhattacharya, S., Huynh-The, T., Chengoden, R., Yenduri, G., Maddikunta, P. K. R., Pham, Q.-V., Gadekallu, T. R., & Liyanage, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3304790 · Cited by 183 · see citing works
Ali, M., Karimipour, H., & Tariq, M. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102355 · Cited by 177 · see citing works
Myrzashova, R., Alsamhi, S. H., Shvetsov, A. V., Hawbani, A., & Wei, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3263598 · Cited by 178 · see citing works
Applications of Federated Learning; Taxonomy, Challenges, and Research Trends
Shaheen, M., Farooq, M. S., Umer, T., & Kim, B.-S. (2022) · Electronics · doi:10.3390/electronics11040670 · Cited by 157 · see citing works
Xu, H., Zhu, T., Zhang, L., Zhou, W., & Yu, P. S. (2024) · ACM Computing Surveys · doi:10.1145/3603620 · Cited by 164 · see citing works
EPPDA: An Efficient Privacy-Preserving Data Aggregation Federated Learning Scheme
Song, J., Wang, W., Gadekallu, T. R., Cao, J., & Liu, Y. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3153519 · Cited by 150 · see citing works
Challenges, Applications and Design Aspects of Federated Learning: A Survey
Rahman, K. M. J., Ahmed, F., Akhter, N., Hasan, M., Amin, R., Aziz, K. E., Islam, A. K. M. M., Mukta, M. S. H., & Islam, A. K. M. N. (2021) · IEEE Access · doi:10.1109/access.2021.3111118 · Cited by 147 · see citing works
Wan, Y., Qu, Y., Ni, W., Xiang, Y., Gao, L., & Hossain, E. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3361451 · Cited by 152 · see citing works
“In recent years, WFL has seen widespread adoption in various fields, including mobile device development, industrial engineering, and healthcare [21], [22], [23], [24].”
BackgroundDrones’ Edge Intelligence Over Smart Environments in B5G: Blockchain and Federated Learning Synergy
Alsamhi, S. H., Almalki, F. A., Afghah, F., Hawbani, A., Shvetsov, A. V., Lee, B., & Song, H. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2021.3132561 · Cited by 139 · see citing works
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
Wu, J., Dong, F., Leung, H., Zhu, Z., Zhou, J., & Drew, S. (2024) · ACM Computing Surveys · doi:10.1145/3659205 · Cited by 130 · see citing works
Challenges and future directions of secure federated learning: a survey
Zhang, K., Song, X., Zhang, C., & Yu, S. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-021-0598-z · Cited by 127 · see citing works
Lead federated neuromorphic learning for wireless edge artificial intelligence
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Self-citationFedHAR: Semi-Supervised Online Learning for Personalized Federated Human Activity Recognition
Yu, H., Chen, Z., Zhang, X., Chen, X., Zhuang, F., Xiong, H., & Cheng, X. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3136853 · Cited by 113 · see citing works
Reviewing Federated Machine Learning and Its Use in Diseases Prediction
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23042112 · Cited by 114 · see citing works
Xu, W., Fang, W., Ding, Y., Zou, M., & Xiong, N. (2021) · IEEE Access · doi:10.1109/access.2021.3063291 · Cited by 112 · see citing works
SCA: Sybil-Based Collusion Attacks of IIoT Data Poisoning in Federated Learning
Xiao, X., Tang, Z., Li, C., Xiao, B., & Li, K. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3172310 · Cited by 99 · see citing works
A Decentralized Federated Learning Approach for Connected Autonomous Vehicles
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Abdel Hakeem, S. A., & Kim, H. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3558849 · Cited by 90 · see citing works
Federated Learning in Robotic and Autonomous Systems
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Blockchain for Securing AI Applications and Open Innovations
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Self-citationA survey for federated learning evaluations: Goals and measures
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Self-citationBlockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging
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A federated learning framework for healthcare IoT devices
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Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey
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Federated learning architecture: Design, implementation, and challenges in distributed AI systems
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Popularity Prediction with Federated Learning for Proactive Caching at Wireless Edge
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Toward Smart Security Enhancement of Federated Learning Networks
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Self-citationFederated learning vulnerabilities, threats and defenses: A systematic review and future directions
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RobustFed: A Truth Inference Approach for Robust Federated Learning
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TinyFedTL: Federated Transfer Learning on Tiny Devices
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- Authors
- Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C.
- Affiliations
- Nanyang Technological University (SG)
- Phenikaa (Vietnam) (VN)
- University of Technology Sydney (AU)
- University of Electronic Science and Technology of China (CN)
- Hong Kong University of Science and Technology (HK)
- ORCID
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2020
- Volume
- 22
- Issue
- 3
- Pages
- 2031-2063
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W3015636663
- Cited by
- 2,565
- References
- 194
- Citations by year
- 2026: 337 · 2025: 409 · 2024: 443 · 2023: 489 · 2022: 416 · 2021: 326 · 2020: 144 · 2019: 1
- FWCI
- 135.14
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion, public-domain)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Research Foundation (NRF), Singapore, through Singapore Energy Market Authority, Energy Resilience (NRF2017EWT-EP003-041, NRF2015-NRF-ISF001-2277)
- Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure (NSoE DeST-SCI2019-0007)
- A*STAR-NTU-SUTD Joint Research Grant Call on Artificial Intelligence for the Future of Manufacturing (RGANS1906, WASP/NTUM4082187(4080))
- Singapore MOE Tier 2 (MOE2014-T2-2-015 ARC4/15, MOE Tier 1 2017-T1-002-007 RG122/17)
- AI Singapore Programme (AISG-GC-2019-003, NRF-NRFI05-2019-0002)
- Alibaba-NTU Singapore Joint Research Institute (Alibaba-NTU-AIR2019B1)
- Nanyang Technological University, Singapore
- Vietnam National Foundation for Science and Technology Development (102.02-2019.305)
- National Natural Science Foundation of China (61631005, U1801261)
- National Basic Research Program of China (2018YFB1801105)
- Higher Education Discipline Innovation Project (B20064)
- Hong Kong CERG (16209715, 16244616)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- federated learning, mobile edge computing, mobile edge networks, data privacy, resource allocation, communication overhead, heterogeneous devices, deep learning, vehicular networks, medical applications
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