mlsanityData to 14 Oct 2026

FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update

arXiv 2108.09081 ↗First posted 20 Aug 2021cs.LG cs.AI cs.DCcited by 4

Not on the map. Why?

Abstract (arXiv)

Federated learning aims to protect users' privacy while performing data analysis from different participants. However, it is challenging to guarantee the training efficiency on heterogeneous systems due to the various computational capabilities and communication bottlenecks. In this work, we propose FedSkel to enable computation-efficient and communication-efficient federated learning on edge devices by only updating the model's essential parts, named skeleton networks. FedSkel is evaluated on real edge devices with imbalanced datasets. Experimental results show that it could achieve up to 5.52× speedups for CONV layers' back-propagation, 1.82× speedups for the whole training process, and reduce 64.8% communication cost, with negligible accuracy loss.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 23 references that are in the map. First posted on arXiv 20 Aug 2021.

Builds on

17 of 23 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2018LEAF: A Benchmark for Federated Settingscited by 792
  6. 2017Federated Multi-Task Learningcited by 672
  7. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
  8. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  9. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  10. 2014Caffe: Convolutional Architecture for Fast Feature Embeddingcited by 288
  11. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 204
  12. 2015Structured Pruning of Deep Convolutional Neural Networkscited by 149
5 more
  1. 2019Federated Learning for Emoji Prediction in a Mobile Keyboardcited by 118
  2. 2015Fast ConvNets Using Group-wise Brain Damagecited by 82
  3. 2020LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasetscited by 60
  4. 2019A Distributed Synchronous SGD Algorithm with Global Top-k Sparsification for Low Bandwidth Networkscited by 37
  5. 2018Distilling Critical Paths in Convolutional Neural Networkscited by 7

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 1.

4 papers.

  1. 2024Tackling Noisy Clients in Federated Learning with End-to-end Label Correctioncited by 12
  2. 2022FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learningcited by 2
  3. 2022FedLesScan: Mitigating Stragglers in Serverless Federated Learningcited by 2
  4. 2025Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Modelscited by 0