mlsanityData to 14 Oct 2026

Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications

arXiv 2310.05269 ↗First posted 8 Oct 2023cs.LG cs.AI cs.CRcited by 3

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Abstract (arXiv)

Robust machine learning (ML) models can be developed by leveraging large volumes of data and distributing the computational tasks across numerous devices or servers. Federated learning (FL) is a technique in the realm of ML that facilitates this goal by utilizing cloud infrastructure to enable collaborative model training among a network of decentralized devices. Beyond distributing the computational load, FL targets the resolution of privacy issues and the reduction of communication costs simultaneously. To protect user privacy, FL requires users to send model updates rather than transmitting large quantities of raw and potentially confidential data. Specifically, individuals train ML models locally using their own data and then upload the results in the form of weights and gradients to the cloud for aggregation into the global model. This strategy is also advantageous in environments with limited bandwidth or high communication costs, as it prevents the transmission of large data volumes. With the increasing volume of data and rising privacy concerns, alongside the emergence of large-scale ML models like Large Language Models (LLMs), FL presents itself as a timely and relevant solution. It is therefore essential to review current FL algorithms to guide future research that meets the rapidly evolving ML demands. This survey provides a comprehensive analysis and comparison of the most recent FL algorithms, evaluating them on various fronts including mathematical frameworks, privacy protection, resource allocation, and applications. Beyond summarizing existing FL methods, this survey identifies potential gaps, open areas, and future challenges based on the performance reports and algorithms used in recent studies. This survey enables researchers to readily identify existing limitations in the FL field for further exploration.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 64 of its 267 references that are in the map. First posted on arXiv 8 Oct 2023.

Builds on

64 of 267 references.

References · 64

  1. 2019Advances and Open Problems in Federated Learningcited by 3,046
  2. 2020Adaptive Federated Optimizationcited by 1,046
  3. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  4. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 160
  5. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 139
  6. 2019Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligencecited by 76
  7. 2020Local Differential Privacy based Federated Learning for Internet of Thingscited by 75
  8. 2019Byzantine-Robust Federated Machine Learning through Adaptive Model Averagingcited by 70
  9. 2020Fast-Convergent Federated Learning with Adaptive Weightingcited by 67
  10. 2021Eluding Secure Aggregation in Federated Learning via Model Inconsistencycited by 67
  11. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
  12. 2020Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Frameworkcited by 59
38 more
  1. 2022Communication-Efficient Adaptive Federated Learningcited by 55
  2. 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
  3. 2020Faster Non-Convex Federated Learning via Global and Local Momentumcited by 48
  4. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  5. 2021DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learningcited by 41
  6. 2021Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learningcited by 39
  7. 2020Dynamic Fusion based Federated Learning for COVID-19 Detectioncited by 36
  8. 2022Joint Privacy Enhancement and Quantization in Federated Learningcited by 35
  9. 2020When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Networkcited by 30
  10. 2020Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approachcited by 29
  11. 2021Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errorscited by 25
  12. 2022Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTcited by 25
  13. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  14. 2018Joint Service Pricing and Cooperative Relay Communication for Federated Learningcited by 22
  15. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  16. 2020Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learningcited by 19
  17. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  18. 2021Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computingcited by 18
  19. 2021Communication-Efficient Federated Learning via Quantized Compressed Sensingcited by 18
  20. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  21. 2021Scalable federated machine learning with FEDncited by 16
  22. 2021Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcarecited by 15
  23. 2020Privacy Preserving Distributed Machine Learning with Federated Learningcited by 13
  24. 2021Faithful Edge Federated Learning: Scalability and Privacycited by 13
  25. 2022Asynchronous Decentralized Learning over Unreliable Wireless Networkscited by 13
  26. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  27. 2020Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networkscited by 11
  28. 2022Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graphcited by 11
  29. 2020FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applicationscited by 10
  30. 2022Adaptive Aggregation For Federated Learningcited by 10
  31. 2022FAIR-FATE: Fair Federated Learning with Momentumcited by 10
  32. 2022Study of the performance and scalability of federated learning for medical imaging with intermittent clientscited by 9
  33. 2022Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Dronescited by 7
  34. 2023A Survey of Trojans in Neural Models of Source Code: Taxonomy and Techniquescited by 7
  35. 2023Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networkscited by 7
  36. 2020LocKedge: Low-Complexity Cyberattack Detection in IoT Edge Computingcited by 6
  37. 2023BFRT: Blockchained Federated Learning for Real-time Traffic Flow Predictioncited by 6
  38. 2020Data Analytics-enabled Intrusion Detection: Evaluations of ToN_IoT Linux Datasetscited by 4

50 most cited of 64 All 64 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environmentscited by 1
  2. 2025PUL-Inter-slice Defender: An Anomaly Detection Solution for Distributed Slice Mobility Attackscited by 0
  3. 2026Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inferencecited by 0