mlsanityData to 7 Oct 2026

FedPrune: Towards Inclusive Federated Learning

arXiv 2110.14205 ↗First posted 27 Oct 2021cs.LGcited by 12

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Abstract (arXiv)

Federated learning (FL) is a distributed learning technique that trains a shared model over distributed data in a privacy-preserving manner. Unfortunately, FL's performance degrades when there is (i) variability in client characteristics in terms of computational and memory resources (system heterogeneity) and (ii) non-IID data distribution across clients (statistical heterogeneity). For example, slow clients get dropped in FL schemes, such as Federated Averaging (FedAvg), which not only limits overall learning but also biases results towards fast clients. We propose FedPrune; a system that tackles this challenge by pruning the global model for slow clients based on their device characteristics. By doing so, slow clients can train a small model quickly and participate in FL which increases test accuracy as well as fairness. By using insights from Central Limit Theorem, FedPrune incorporates a new aggregation technique that achieves robust performance over non-IID data. Experimental evaluation shows that Fed- Prune provides robust convergence and better fairness compared to Federated Averaging.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 27 Oct 2021.

Builds on

17 of 37 references.

References · 17

  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. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2019Towards Federated Learning at Scale: System Designcited by 826
  5. 2018LEAF: A Benchmark for Federated Settingscited by 780
  6. 2017Federated Multi-Task Learningcited by 669
  7. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  8. 2019Agnostic Federated Learningcited by 451
  9. 2019Fair Resource Allocation in Federated Learningcited by 389
  10. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 363
  11. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  12. 2021A Field Guide to Federated Optimizationcited by 312
5 more
  1. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
  2. 2017EMNIST: an extension of MNIST to handwritten letterscited by 212
  3. 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 162
  4. 2021Asynchronous Federated Learning on Heterogeneous Devices: A Surveycited by 93
  5. 2016Performance Characterization of a Commercial Video Streaming Servicecited by 1

Built on by

Cited by year

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

12 papers.

  1. 2022A Survey on Heterogeneous Federated Learningcited by 42
  2. 2022Sparse Random Networks for Communication-Efficient Federated Learningcited by 32
  3. 2023TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Trainingcited by 27
  4. 2022Minimax Demographic Group Fairness in Federated Learningcited by 24
  5. 2024FedMef: Towards Memory-efficient Federated Dynamic Pruningcited by 14
  6. 2022Distributed Pruning Towards Tiny Neural Networks in Federated Learningcited by 11
  7. 2025FedRTS: Federated Robust Pruning via Combinatorial Thompson Samplingcited by 4
  8. 2024Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Trainingcited by 3
  9. 2024FedSpaLLM: Federated Pruning of Large Language Modelscited by 3
  10. 2024SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learningcited by 1
  11. 2024subMFL: Compatiple subModel Generation for Federated Learning in Device Heterogenous Environmentcited by 0
  12. 2024Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learningcited by 0