mlsanityData to 7 Oct 2026

FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server

arXiv 2204.11536 ↗First posted 25 Apr 2022cs.DC cs.AIcited by 29

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Abstract (arXiv)

Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical challenges, i.e., limited computational resources and low training efficiency. In this paper, we propose a novel FL framework, i.e., FedDUAP, with two original contributions, to exploit the insensitive data on the server and the decentralized data in edge devices to further improve the training efficiency. First, a dynamic server update algorithm is designed to exploit the insensitive data on the server, in order to dynamically determine the optimal steps of the server update for improving the convergence and accuracy of the global model. Second, a layer-adaptive model pruning method is developed to perform unique pruning operations adapted to the different dimensions and importance of multiple layers, to achieve a good balance between efficiency and effectiveness. By integrating the two original techniques together, our proposed FL model, FedDUAP, significantly outperforms baseline approaches in terms of accuracy (up to 4.8% higher), efficiency (up to 2.8 times faster), and computational cost (up to 61.9% smaller).

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 16 of its 35 references that are in the map. First posted on arXiv 25 Apr 2022.

Builds on

16 of 35 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2018Federated Learning with Non-IID Datacited by 1,099
  7. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  8. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  9. 2020HRank: Filter Pruning using High-Rank Feature Mapcited by 291
  10. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
  11. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
  12. 2020Practical One-Shot Federated Learning for Cross-Silo Settingcited by 76
4 more
  1. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  2. 2019Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Datacited by 38
  3. 2021Game of Gradients: Mitigating Irrelevant Clients in Federated Learningcited by 35
  4. 2021Efficient Device Scheduling with Multi-Job Federated Learningcited by 29

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 9; 2024: 9; 2025: 6; 2026: 1.

29 papers.

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  1. 2023Structured Pruning for Deep Convolutional Neural Networks: A surveycited by 122
  2. 2023Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimizationcited by 65
  3. 2022Accelerated Federated Learning with Decoupled Adaptive Optimizationcited by 23
  4. 2022FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneitycited by 20
  5. 2022Multi-Job Intelligent Scheduling with Cross-Device Federated Learningcited by 20
  6. 2023AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devicescited by 15
  7. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  8. 2023Efficient Asynchronous Federated Learning with Sparsification and Quantizationcited by 9
  9. 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learningcited by 8
  10. 2024Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Datacited by 6
  11. 2024Fisher Information-based Efficient Curriculum Federated Learning with Large Language Modelscited by 6
  12. 2022Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resourcescited by 5
17 more
  1. 2023Edge-cloud Collaborative Learning with Federated and Centralized Featurescited by 5
  2. 2025SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignmentcited by 3
  3. 2023FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropoutcited by 2
  4. 2023On Mask-based Image Set Desensitization with Recognition Supportcited by 2
  5. 2024Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidthcited by 2
  6. 2025Efficient Federated Learning with Timely Update Disseminationcited by 2
  7. 2025Efficient Federated Learning with Heterogeneous Data and Adaptive Dropoutcited by 2
  8. 2024FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantizationcited by 1
  9. 2024Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasetscited by 1
  10. 2024Automatic Structured Pruning for Efficient Architecture in Federated Learningcited by 1
  11. 2023A Unified Momentum-based Paradigm of Decentralized SGD for Non-Convex Models and Heterogeneous Datacited by 0
  12. 2023Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costscited by 0
  13. 2024Towards Fairness in Provably Communication-Efficient Federated Recommender Systemscited by 0
  14. 2025SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Trainingcited by 0
  15. 2025TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environmentscited by 0
  16. 2025PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraintscited by 0
  17. 2026FedASAP: Activation Statistics-driven Structured Adaptive Pruning for Efficient Personalized Federated Learning for Lesion Segmentation on brain MRIcited by 0