mlsanityData to 7 Oct 2026

FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learning

arXiv 2303.06360 ↗First posted 11 Mar 2023cs.LG cs.AI cs.DCcited by 7

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Abstract (arXiv)

Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for distributed learning. In this work, we mainly focus on the optimization of computation and communication in FL from a view of pruning. By adopting layer-wise pruning in local training and federated updating, we formulate an explicit FL pruning framework, FedLP (Federated Layer-wise Pruning), which is model-agnostic and universal for different types of deep learning models. Two specific schemes of FedLP are designed for scenarios with homogeneous local models and heterogeneous ones. Both theoretical and experimental evaluations are developed to verify that FedLP relieves the system bottlenecks of communication and computation with marginal performance decay. To the best of our knowledge, FedLP is the first framework that formally introduces the layer-wise pruning into FL. Within the scope of federated learning, more variants and combinations can be further designed based on FedLP.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 11 Mar 2023.

Builds on

14 of 21 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  4. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  5. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
  6. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  7. 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 162
  8. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
  9. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  10. 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
  11. 2021Layer-wise Adaptive Model Aggregation for Scalable Federated Learningcited by 35
  12. 2022FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learningcited by 17
2 more
  1. 2020Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computingcited by 9
  2. 2021How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learningcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 3; 2026: 2.

7 papers.

  1. 2023Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneitycited by 10
  2. 2023FedNC: A Secure and Efficient Federated Learning Method with Network Codingcited by 2
  3. 2025PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learningcited by 1
  4. 2025Onboard Optimization and Learning: A Surveycited by 1
  5. 2025FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensingcited by 1
  6. 2026EdgeFLow: Serverless Federated Learning via Sequential Model Migration in Edge Networkscited by 1
  7. 2026FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inferencecited by 0