mlsanityData to 7 Oct 2026

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

arXiv 2110.04160 ↗First posted 8 Oct 2021cs.LG cs.AIcited by 6

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Abstract (arXiv)

In the recent years, generation of data have escalated to extensive dimensions and big data has emerged as a propelling force in the development of various machine learning advances and internet-of-things (IoT) devices. In this regard, the analytical and learning tools that transport data from several sources to a central cloud for its processing, training, and storage enable realization of the potential of big data. Nevertheless, since the data may contain sensitive information like banking account information, government information, and personal information, these traditional techniques often raise serious privacy concerns. To overcome such challenges, Federated Learning (FL) emerges as a sub-field of machine learning that focuses on scenarios where several entities (commonly termed as clients) work together to train a model while maintaining the decentralisation of their data. Although enormous efforts have been channelized for such studies, there still exists a gap in the literature wherein an extensive review of FL in the realm of big data services remains unexplored. The present paper thus emphasizes on the use of FL in handling big data and related services which encompasses comprehensive review of the potential of FL in big data acquisition, storage, big data analytics and further privacy preservation. Subsequently, the potential of FL in big data applications, such as smart city, smart healthcare, smart transportation, smart grid, and social media are also explored. The paper also highlights various projects pertaining to FL-big data and discusses the associated challenges related to such implementations. This acts as a direction of further research encouraging the development of plausible solutions.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 49 of its 246 references that are in the map. First posted on arXiv 8 Oct 2021.

Builds on

49 of 246 references.

References · 49

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
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37 more
  1. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  2. 2019A Survey on Distributed Machine Learningcited by 187
  3. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  4. 2020Threats to Federated Learning: A Surveycited by 171
  5. 2020A Survey on Knowledge Graph-Based Recommender Systemscited by 170
  6. 2021Federated Learning for Open Bankingcited by 138
  7. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  8. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  9. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
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  12. 2021Federated Learning: Opportunities and Challengescited by 83
  13. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  14. 2019Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networkscited by 79
  15. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
  16. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
  17. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  18. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  19. 2019Energy Demand Prediction with Federated Learning for Electric Vehicle Networkscited by 46
  20. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  21. 2020A Secure Federated Learning Framework for 5G Networkscited by 32
  22. 2020Federated Learning for 6G Communications: Challenges, Methods, and Future Directionscited by 32
  23. 2020Communication-Efficient Federated Learning with Compensated Overlap-FedAvgcited by 31
  24. 2020Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approachcited by 29
  25. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  26. 2021IPLS : A Framework for Decentralized Federated Learningcited by 26
  27. 2020Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Servicescited by 18
  28. 2020VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoTcited by 17
  29. 2021UAV Communications for Sustainable Federated Learningcited by 15
  30. 2020Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifierscited by 13
  31. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  32. 2020Towards Ubiquitous AI in 6G with Federated Learningcited by 5
  33. 2020Pocket Diagnosis: Secure Federated Learning against Poisoning Attack in the Cloudcited by 5
  34. 2021Secure and Privacy-Preserving Federated Learning via Co-Utilitycited by 5
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 1; 2025: 0; 2026: 1.

6 papers.

  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2021Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challengescited by 5
  3. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  4. 2021ARFED: Attack-Resistant Federated averaging based on outlier eliminationcited by 1
  5. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  6. 2026SNAP: A Self-Consistent Agreement Principle with Application to Robust Computationcited by 0