mlsanityData to 7 Oct 2026

The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning

arXiv 2111.05326 ↗First posted 9 Nov 2021cs.LGcited by 23

Not on the map. Why?

Abstract (arXiv)

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 140 of its 374 references that are in the map. First posted on arXiv 9 Nov 2021.

Builds on

140 of 374 references.

References · 140

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  5. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  6. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  7. 2019Advances and Open Problems in Federated Learningcited by 3,021
  8. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  9. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  10. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  11. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  12. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
38 more
  1. 2016Variational Inference: A Review for Statisticianscited by 1,466
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2018Averaging Weights Leads to Wider Optima and Better Generalizationcited by 1,194
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2020Adaptive Federated Optimizationcited by 1,036
  6. 2017Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoningcited by 1,012
  7. 2019Deep Leakage from Gradientscited by 1,004
  8. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  9. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  10. 2018How To Backdoor Federated Learningcited by 745
  11. 2014Certifying and removing disparate impactcited by 715
  12. 2019A Closer Look at Few-shot Classificationcited by 679
  13. 2017Federated Multi-Task Learningcited by 669
  14. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  15. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
  16. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  17. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  18. 2020Federated Learning with Matched Averagingcited by 564
  19. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  20. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 555
  21. 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 555
  22. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  23. 2019Federated Learning with Personalization Layerscited by 533
  24. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  25. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  26. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 498
  27. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 496
  28. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  29. 2019Agnostic Federated Learningcited by 451
  30. 2018Conditional Neural Processescited by 421
  31. 2019How to Fine-Tune BERT for Text Classification?cited by 409
  32. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  33. 2019Fair Resource Allocation in Federated Learningcited by 389
  34. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  35. 2017Meta-SGD: Learning to Learn Quickly for Few-Shot Learningcited by 377
  36. 2020Adaptive Personalized Federated Learningcited by 353
  37. 2018Not All Samples Are Created Equal: Deep Learning with Importance Samplingcited by 323
  38. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322

50 most cited of 140 All 140 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 10; 2024: 4; 2025: 3; 2026: 1.

23 papers.

Sort:

  1. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  2. 2022Personalized PCA: Decoupling Shared and Unique Featurescited by 17
  3. 2025LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimizationcited by 13
  4. 2022Application of federated learning in manufacturingcited by 11
  5. 2022Federated Data Analytics: A Study on Linear Modelscited by 9
  6. 2023Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Designcited by 9
  7. 2021Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modelingcited by 8
  8. 2023Personalized Dictionary Learning for Heterogeneous Datasetscited by 5
  9. 2024Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspectivecited by 5
  10. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  11. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  12. 2023Federated Multilinear Principal Component Analysis with Applications in Prognosticscited by 3
11 more
  1. 2024Federated Automatic Latent Variable Selection in Multi-output Gaussian Processescited by 3
  2. 2023Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Datacited by 2
  3. 2023A Federated Data Fusion-Based Prognostic Model for Applications with Multi-Stream Incomplete Signalscited by 1
  4. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  5. 2023Differentially Private Distributed Estimation and Learningcited by 0
  6. 2023Learning Driver Models for Automated Vehicles via Knowledge Sharing and Personalizationcited by 0
  7. 2023Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Thingscited by 0
  8. 2024FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learningcited by 0
  9. 2025A Collaborative Process Parameter Recommender System for Fleets of Networked Manufacturing Machines -- with Application to 3D Printingcited by 0
  10. 2025ARCO-BO: Adaptive Resource-aware COllaborative Bayesian Optimization for Heterogeneous Multi-Agent Designcited by 0
  11. 2026Personalized Federated Hierarchical Gaussian Processes for Privacy-Preserving Modeling of Heterogeneous Distributed Systemscited by 0