# FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning

arXiv: https://arxiv.org/abs/2107.04271 · HTML: https://mlsanity.com/paper/2107.04271/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 46 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 27 of 27)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Deep Exploration via Bootstrapped DQN](https://mlsanity.com/paper/1602.04621/) (2016)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things](https://mlsanity.com/paper/2003.13376/) (2020)
- [Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy](https://mlsanity.com/paper/1806.07840/) (2018)
- [Performance Optimization for Federated Person Re-identification via Benchmark Analysis](https://mlsanity.com/paper/2008.11560/) (2020)
- [Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity](https://mlsanity.com/paper/2005.12326/) (2020)
- [Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection](https://mlsanity.com/paper/2106.07976/) (2021)
- [Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art](https://mlsanity.com/paper/2002.10610/) (2020)
- [Coded Federated Learning](https://mlsanity.com/paper/2002.09574/) (2020)
- [Taming Momentum in a Distributed Asynchronous Environment](https://mlsanity.com/paper/1907.11612/) (2019)
- [Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices](https://mlsanity.com/paper/1911.04559/) (2019)
- [Big Data Privacy in the Internet of Things Era](https://mlsanity.com/paper/1412.8339/) (2014)
- [A Review of Privacy-preserving Federated Learning for the Internet-of-Things](https://mlsanity.com/paper/2004.11794/) (2020)

## Built on by (most cited first, 26 of 26)

- [FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization](https://mlsanity.com/paper/2111.14655/) (2021)
- [FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout](https://mlsanity.com/paper/2307.02623/) (2023)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing](https://mlsanity.com/paper/2407.08462/) (2024)
- [Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions](https://mlsanity.com/paper/2207.09611/) (2022)
- [Aergia: Leveraging Heterogeneity in Federated Learning Systems](https://mlsanity.com/paper/2210.06154/) (2022)
- [An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneity](https://mlsanity.com/paper/2305.19831/) (2023)
- [Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case study](https://mlsanity.com/paper/2506.00499/) (2025)
- [Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting](https://mlsanity.com/paper/2410.11577/) (2024)
- [FedFly: Towards Migration in Edge-based Distributed Federated Learning](https://mlsanity.com/paper/2111.01516/) (2021)
- [Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning](https://mlsanity.com/paper/2210.10311/) (2022)
- [Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning](https://mlsanity.com/paper/2307.11532/) (2023)
- [MP-SL: Multihop Parallel Split Learning](https://mlsanity.com/paper/2402.00208/) (2024)
- [PiPar: Pipeline Parallelism for Collaborative Machine Learning](https://mlsanity.com/paper/2302.12803/) (2022)
- [Ampere: Communication-Efficient and High-Accuracy Split Federated Learning](https://mlsanity.com/paper/2507.07130/) (2025)
- [Optimal Batch Allocation for Wireless Federated Learning](https://mlsanity.com/paper/2404.02395/) (2024)
- [Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning](https://mlsanity.com/paper/2405.02360/) (2024)
- [Asynchronous Byzantine Federated Learning](https://mlsanity.com/paper/2406.01438/) (2024)
- [Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and Fusion](https://mlsanity.com/paper/2207.11447/) (2022)
- [Reducing Impacts of System Heterogeneity in Federated Learning using Weight Update Magnitudes](https://mlsanity.com/paper/2208.14808/) (2022)
- [Where is the Testbed for my Federated Learning Research?](https://mlsanity.com/paper/2407.14154/) (2024)
- [EMO: Edge Model Overlays to Scale Model Size in Federated Learning](https://mlsanity.com/paper/2504.00726/) (2025)
- [FedOptima: Optimizing Resource Utilization in Federated Learning](https://mlsanity.com/paper/2504.13850/) (2025)
- [Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation](https://mlsanity.com/paper/2509.01868/) (2025)
- [CLIP: Client-Side Invariant Pruning for Mitigating Stragglers in Secure Federated Learning](https://mlsanity.com/paper/2510.16694/) (2025)
- [QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning](https://mlsanity.com/paper/2606.09869/) (2026)

## 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.