mlsanityData to 7 Oct 2026

Federated Learning: Opportunities and Challenges

arXiv 2101.05428 ↗First posted 14 Jan 2021cs.LG cs.DCcited by 83

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Abstract (arXiv)

Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple devices collaboratively learn a machine learning model without sharing their private data under the supervision of a central server. This offers ample opportunities in critical domains such as healthcare, finance etc, where it is risky to share private user information to other organisations or devices. While FL appears to be a promising Machine Learning (ML) technique to keep the local data private, it is also vulnerable to attacks like other ML models. Given the growing interest in the FL domain, this report discusses the opportunities and challenges in federated learning.

Key facts. Cited by 83 papers in the map (data through 7 Oct 2026). Builds on 23 of its 39 references that are in the map. First posted on arXiv 14 Jan 2021.

Builds on

23 of 39 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2019Towards Federated Learning at Scale: System Designcited by 826
  6. 2018How To Backdoor Federated Learningcited by 745
  7. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  8. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  9. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 266
  10. 2019Asynchronous Federated Optimizationcited by 264
  11. 2019Can You Really Backdoor Federated Learning?cited by 250
  12. 2019One-Shot Federated Learningcited by 124
11 more
  1. 2020Distilled One-Shot Federated Learningcited by 108
  2. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  3. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  4. 2018A Review of Challenges and Opportunities in Machine Learning for Healthcited by 67
  5. 2020Data Poisoning Attacks on Federated Machine Learningcited by 58
  6. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
  7. 2019Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approachcited by 45
  8. 2019BAFFLE : Blockchain Based Aggregator Free Federated Learningcited by 38
  9. 2020FLaaS: Federated Learning as a Servicecited by 22
  10. 2020Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noisecited by 21
  11. 2020BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecturecited by 20

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 9; 2023: 15; 2024: 27; 2025: 17; 2026: 10.

83 papers.

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  1. 2023Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Modelcited by 33
  2. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directionscited by 30
  3. 2022PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protectioncited by 26
  4. 2022Industry-Scale Orchestrated Federated Learning for Drug Discoverycited by 19
  5. 2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMscited by 19
  6. 2021Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Trainingcited by 17
  7. 2022Tighter Regret Analysis and Optimization of Online Federated Learningcited by 14
  8. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  9. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  10. 2022Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutationcited by 10
  11. 2024Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detectioncited by 8
  12. 2024FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Datacited by 7
38 more
  1. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  2. 2023Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMIcited by 6
  3. 2023Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectivescited by 6
  4. 2023A Theoretical Insight into Attack and Defense of Gradient Leakage in Transformercited by 6
  5. 2024An Efficient Privacy-aware Split Learning Framework for Satellite Communicationscited by 6
  6. 2023One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Modelscited by 5
  7. 2023Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networkscited by 5
  8. 2024Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gaincited by 5
  9. 2024Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbotscited by 5
  10. 2025FW-Merging: Scaling Model Merging with Frank-Wolfe Optimizationcited by 5
  11. 2021Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sourcescited by 4
  12. 2022Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applicationscited by 4
  13. 2024CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Modelscited by 4
  14. 2024On the Efficiency of Privacy Attacks in Federated Learningcited by 3
  15. 2024I/O in Machine Learning Applications on HPC Systems: A 360-degree Surveycited by 3
  16. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  17. 2024Multi-PA: A Multi-perspective Benchmark on Privacy Assessment for Large Vision-Language Modelscited by 3
  18. 2025Robust Distributed Estimation: Extending Gossip Algorithms to Ranking and Trimmed Meanscited by 3
  19. 2022FedLesScan: Mitigating Stragglers in Serverless Federated Learningcited by 2
  20. 2023Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Reviewcited by 2
  21. 2023Federated Learning with Nonvacuous Generalisation Boundscited by 2
  22. 2024Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detectioncited by 2
  23. 2025FedEFM: Federated Endovascular Foundation Model with Unseen Datacited by 2
  24. 2025Exploring Incremental Unlearning: Techniques, Challenges, and Future Directionscited by 2
  25. 2021OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learningcited by 1
  26. 2021On-Demand Unlabeled Personalized Federated Learningcited by 1
  27. 2023FedST: Secure Federated Shapelet Transformation for Time Series Classificationcited by 1
  28. 2023Attacks on fairness in Federated Learningcited by 1
  29. 2023Mitigating Data Injection Attacks on Federated Learningcited by 1
  30. 2024A review on different techniques used to combat the non-IID and heterogeneous nature of data in FLcited by 1
  31. 2024FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learningcited by 1
  32. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  33. 2024Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Processcited by 1
  34. 2024FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regionscited by 1
  35. 2024Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guaranteescited by 1
  36. 2024FedCL-Ensemble Learning: A Framework of Federated Continual Learning with Ensemble Transfer Learning Enhanced for Alzheimer's MRI Classifications while Preserving Privacycited by 1
  37. 2025A Federated Random Forest Solution for Secure Distributed Machine Learningcited by 1
  38. 2025ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learningcited by 1

50 most cited of 83 All 83 (JSON)