mlsanityData to 7 Oct 2026

FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning

arXiv 2107.04271 ↗First posted 9 Jul 2021cs.DC cs.LGcited by 26

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Abstract (arXiv)

Applying Federated Learning (FL) on Internet-of-Things devices is necessitated by the large volumes of data they produce and growing concerns of data privacy. However, there are three challenges that need to be addressed to make FL efficient: (i) execution on devices with limited computational capabilities, (ii) accounting for stragglers due to computational heterogeneity of devices, and (iii) adaptation to the changing network bandwidths. This paper presents FedAdapt, an adaptive offloading FL framework to mitigate the aforementioned challenges. FedAdapt accelerates local training in computationally constrained devices by leveraging layer offloading of deep neural networks (DNNs) to servers. Further, FedAdapt adopts reinforcement learning based optimization and clustering to adaptively identify which layers of the DNN should be offloaded for each individual device on to a server to tackle the challenges of computational heterogeneity and changing network bandwidth. Experimental studies are carried out on a lab-based testbed and it is demonstrated that by offloading a DNN from the device to the server FedAdapt reduces the training time of a typical IoT device by over half compared to classic FL. The training time of extreme stragglers and the overall training time can be reduced by up to 57%. Furthermore, with changing network bandwidth, FedAdapt is demonstrated to reduce the training time by up to 40% when compared to classic FL, without sacrificing accuracy.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 27 of its 46 references that are in the map. First posted on arXiv 9 Jul 2021.

Builds on

27 of 46 references.

References · 27

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15 more
  1. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 225
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 6; 2023: 3; 2024: 8; 2025: 6; 2026: 1.

26 papers.

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  1. 2021FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorizationcited by 31
  2. 2023FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropoutcited by 16
  3. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  4. 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computingcited by 15
  5. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  6. 2022Aergia: Leveraging Heterogeneity in Federated Learning Systemscited by 6
  7. 2023An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneitycited by 5
  8. 2024Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splittingcited by 4
  9. 2025Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case studycited by 4
  10. 2021FedFly: Towards Migration in Edge-based Distributed Federated Learningcited by 3
  11. 2022Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learningcited by 3
  12. 2023Training Latency Minimization for Model-Splitting Allowed Federated Edge Learningcited by 3
14 more
  1. 2024MP-SL: Multihop Parallel Split Learningcited by 3
  2. 2022PiPar: Pipeline Parallelism for Collaborative Machine Learningcited by 2
  3. 2024Optimal Batch Allocation for Wireless Federated Learningcited by 1
  4. 2024Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learningcited by 1
  5. 2024Asynchronous Byzantine Federated Learningcited by 1
  6. 2025Ampere: Communication-Efficient and High-Accuracy Split Federated Learningcited by 1
  7. 2022Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and Fusioncited by 0
  8. 2022Reducing Impacts of System Heterogeneity in Federated Learning using Weight Update Magnitudescited by 0
  9. 2024Where is the Testbed for my Federated Learning Research?cited by 0
  10. 2025EMO: Edge Model Overlays to Scale Model Size in Federated Learningcited by 0
  11. 2025FedOptima: Optimizing Resource Utilization in Federated Learningcited by 0
  12. 2025Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluationcited by 0
  13. 2025CLIP: Client-Side Invariant Pruning for Mitigating Stragglers in Secure Federated Learningcited by 0
  14. 2026QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learningcited by 0