# Model Pruning Enables Efficient Federated Learning on Edge Devices

arXiv: https://arxiv.org/abs/1909.12326 · HTML: https://mlsanity.com/paper/1909.12326/

## Key facts

- Cited by 222 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 30 of 222)

- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout](https://mlsanity.com/paper/2102.13451/) (2021)
- [Structured Pruning for Deep Convolutional Neural Networks: A survey](https://mlsanity.com/paper/2303.00566/) (2023)
- [What Do We Mean by Generalization in Federated Learning?](https://mlsanity.com/paper/2110.14216/) (2021)
- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [FedLab: A Flexible Federated Learning Framework](https://mlsanity.com/paper/2107.11621/) (2021)
- [Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better](https://mlsanity.com/paper/2112.09824/) (2021)
- [Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models](https://mlsanity.com/paper/2305.11414/) (2023)
- [ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity](https://mlsanity.com/paper/2208.02507/) (2022)
- [DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning](https://mlsanity.com/paper/2111.00465/) (2021)
- [Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2307.09182/) (2023)
- [pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning](https://mlsanity.com/paper/2310.13283/) (2023)
- [Efficient Personalized Federated Learning via Sparse Model-Adaptation](https://mlsanity.com/paper/2305.02776/) (2023)
- [Layer-wise Adaptive Model Aggregation for Scalable Federated Learning](https://mlsanity.com/paper/2110.10302/) (2021)
- [Advances and Open Challenges in Federated Foundation Models](https://mlsanity.com/paper/2404.15381/) (2024)
- [Sparse Random Networks for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2209.15328/) (2022)
- [FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server](https://mlsanity.com/paper/2204.11536/) (2022)
- [Why Batch Normalization Damage Federated Learning on Non-IID Data?](https://mlsanity.com/paper/2301.02982/) (2023)
- [A Survey on Efficient Federated Learning Methods for Foundation Model Training](https://mlsanity.com/paper/2401.04472/) (2024)
- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [Cost-Effective Federated Learning in Mobile Edge Networks](https://mlsanity.com/paper/2109.05411/) (2021)
- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction](https://mlsanity.com/paper/2310.08670/) (2023)
- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction](https://mlsanity.com/paper/2407.19389/) (2024)
- [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021)
- [Ten Challenging Problems in Federated Foundation Models](https://mlsanity.com/paper/2502.12176/) (2025)
- [Communication-Efficient Split Learning via Adaptive Feature-Wise Compression](https://mlsanity.com/paper/2307.10805/) (2023)
- [Architectural Patterns for the Design of Federated Learning Systems](https://mlsanity.com/paper/2101.02373/) (2021)

## Abstract (arXiv)

Federated learning (FL) allows model training from local data collected by edge/mobile devices while preserving data privacy, which has wide applicability to image and vision applications. A challenge is that client devices in FL usually have much more limited computation and communication resources compared to servers in a datacenter. To overcome this challenge, we propose PruneFL -- a novel FL approach with adaptive and distributed parameter pruning, which adapts the model size during FL to reduce both communication and computation overhead and minimize the overall training time, while maintaining a similar accuracy as the original model. PruneFL includes initial pruning at a selected client and further pruning as part of the FL process. The model size is adapted during this process, which includes maximizing the approximate empirical risk reduction divided by the time of one FL round. Our experiments with various datasets on edge devices (e.g., Raspberry Pi) show that: (i) we significantly reduce the training time compared to conventional FL and various other pruning-based methods; (ii) the pruned model with automatically determined size converges to an accuracy that is very similar to the original model, and it is also a lottery ticket of the original model.