# Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey

arXiv: https://arxiv.org/abs/2307.09182 · HTML: https://mlsanity.com/paper/2307.09182/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 73 of its 115 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 30 of 73)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients](https://mlsanity.com/paper/2010.01264/) (2020)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Model Pruning Enables Efficient Federated Learning on Edge Devices](https://mlsanity.com/paper/1909.12326/) (2019)
- [EMNIST: an extension of MNIST to handwritten letters](https://mlsanity.com/paper/1702.05373/) (2017)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout](https://mlsanity.com/paper/2102.13451/) (2021)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)

## Built on by (most cited first, 30 of 40)

- [The Future of Large Language Model Pre-training is Federated](https://mlsanity.com/paper/2405.10853/) (2024)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Personalized Federated Learning via Feature Distribution Adaptation](https://mlsanity.com/paper/2411.00329/) (2024)
- [Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations](https://mlsanity.com/paper/2405.09839/) (2024)
- [Green Federated Learning: A new era of Green Aware AI](https://mlsanity.com/paper/2409.12626/) (2024)
- [Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration](https://mlsanity.com/paper/2409.18461/) (2024)
- [Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning](https://mlsanity.com/paper/2507.10348/) (2025)
- [Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates](https://mlsanity.com/paper/2403.18375/) (2024)
- [Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective](https://mlsanity.com/paper/2405.20431/) (2024)
- [SignSGD with Federated Voting](https://mlsanity.com/paper/2403.16372/) (2024)
- [Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?](https://mlsanity.com/paper/2411.06618/) (2024)
- [Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization](https://mlsanity.com/paper/2411.18385/) (2024)
- [A survey on Clustered Federated Learning: Taxonomy, Analysis and Applications](https://mlsanity.com/paper/2501.17512/) (2025)
- [FedHQ: Hybrid Runtime Quantization for Federated Learning](https://mlsanity.com/paper/2505.11982/) (2025)
- [Collaborative Adaptive Curriculum for Progressive Knowledge Distillation](https://mlsanity.com/paper/2603.20296/) (2026)
- [Federated Learning with Flexible Architectures](https://mlsanity.com/paper/2406.09877/) (2024)
- [Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices](https://mlsanity.com/paper/2411.07826/) (2024)
- [Aggregation on Learnable Manifolds for Asynchronous Federated Optimization](https://mlsanity.com/paper/2503.14396/) (2025)
- [Adaptive Deadline and Batch Layered Synchronized Federated Learning](https://mlsanity.com/paper/2505.23973/) (2025)
- [CRAD: Class-wise Reliability-Aware Distillation for Decentralized Heterogeneous Federated Learning](https://mlsanity.com/paper/2609.00446/) (2026)
- [Federated Computing -- Survey on Building Blocks, Extensions and Systems](https://mlsanity.com/paper/2404.02779/) (2024)
- [FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2406.19050/) (2024)
- [On Sampling Strategies for Spectral Model Sharding](https://mlsanity.com/paper/2410.24106/) (2024)
- [Predicting Quality of Video Gaming Experience Using Global-Scale Telemetry Data and Federated Learning](https://mlsanity.com/paper/2412.08950/) (2024)
- [GeFL: Model-Agnostic Federated Learning with Generative Models](https://mlsanity.com/paper/2412.18460/) (2024)
- [Efficient Zeroth-Order Federated Finetuning of Language Models on Resource-Constrained Devices](https://mlsanity.com/paper/2502.10239/) (2025)
- [Federated Smoothing ADMM for Localization](https://mlsanity.com/paper/2503.09497/) (2025)
- [EMO: Edge Model Overlays to Scale Model Size in Federated Learning](https://mlsanity.com/paper/2504.00726/) (2025)
- [Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs](https://mlsanity.com/paper/2504.11808/) (2025)
- [CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classification](https://mlsanity.com/paper/2510.17584/) (2025)

## Abstract (arXiv)

With an increasing number of smart devices like internet of things (IoT) devices deployed in the field, offloadingtraining of neural networks (NNs) to a central server becomes more and more infeasible. Recent efforts toimprove users' privacy have led to on-device learning emerging as an alternative. However, a model trainedonly on a single device, using only local data, is unlikely to reach a high accuracy. Federated learning (FL)has been introduced as a solution, offering a privacy-preserving trade-off between communication overheadand model accuracy by sharing knowledge between devices but disclosing the devices' private data. Theapplicability and the benefit of applying baseline FL are, however, limited in many relevant use cases dueto the heterogeneity present in such environments. In this survey, we outline the heterogeneity challengesFL has to overcome to be widely applicable in real-world applications. We especially focus on the aspect ofcomputation heterogeneity among the participating devices and provide a comprehensive overview of recentworks on heterogeneity-aware FL. We discuss two groups: works that adapt the NN architecture and worksthat approach heterogeneity on a system level, covering Federated Averaging (FedAvg), distillation, and splitlearning-based approaches, as well as synchronous and asynchronous aggregation schemes.