mlsanityData to 7 Oct 2026

Adaptive Federated Learning in Resource Constrained Edge Computing Systems

arXiv 1804.05271 ↗First posted 14 Apr 2018cs.DC cs.LG math.OCcited by 408

Not on the map. Why?

Abstract (arXiv)

Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.

Key facts. Cited by 408 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Apr 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 117; 2022: 78; 2023: 77; 2024: 60; 2025: 52; 2026: 24.

408 papers.

Sort:

By area:

  1. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 192
  2. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  3. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  4. 2024Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Modelscited by 114
  5. 2021Federated Graph Classification over Non-IID Graphscited by 113
  6. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  7. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  8. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 80
  9. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 79
  10. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  11. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  12. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
38 more
  1. 2022FedNest: Federated Bilevel, Minimax, and Compositional Optimizationcited by 60
  2. 2021Federated Multi-Armed Banditscited by 59
  3. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 55
  4. 2021Decentralized Federated Learning: Balancing Communication and Computing Costscited by 54
  5. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  6. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 50
  7. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  8. 2021Flexible Clustered Federated Learning for Client-Level Data Distribution Shiftcited by 47
  9. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  10. 2022Federated Learning via Inexact ADMMcited by 44
  11. 2021Anarchic Federated Learningcited by 42
  12. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
  13. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  14. 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
  15. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 39
  16. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  17. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  18. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  19. 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
  20. 2022FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participationcited by 35
  21. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
  22. 2022Multi-Level Branched Regularization for Federated Learningcited by 34
  23. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  24. 2021Towards Model Agnostic Federated Learning Using Knowledge Distillationcited by 31
  25. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  26. 2022Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimizationcited by 28
  27. 2022TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernelscited by 26
  28. 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 25
  29. 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
  30. 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
  31. 2022Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTcited by 25
  32. 2023Federated Domain Generalization: A Surveycited by 25
  33. 2023FwdLLM: Efficient FedLLM using Forward Gradientcited by 25
  34. 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 24
  35. 2022Communication-Efficient Federated Learning with Accelerated Client Gradientcited by 24
  36. 2023Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reductioncited by 24
  37. 2023Green Edge AI: A Contemporary Surveycited by 24
  38. 2023GradientCoin: A Peer-to-Peer Decentralized Large Language Modelscited by 23

50 most cited of 408 All 408 (JSON)