mlsanityData to 7 Oct 2026

Federated learning compression designed for lightweight communications

arXiv 2310.14693 ↗First posted 23 Oct 2023cs.LG eess.SPcited by 2

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Abstract (arXiv)

Federated Learning (FL) is a promising distributed method for edge-level machine learning, particularly for privacysensitive applications such as those in military and medical domains, where client data cannot be shared or transferred to a cloud computing server. In many use-cases, communication cost is a major challenge in FL due to its natural intensive network usage. Client devices, such as smartphones or Internet of Things (IoT) nodes, have limited resources in terms of energy, computation, and memory. To address these hardware constraints, lightweight models and compression techniques such as pruning and quantization are commonly adopted in centralised paradigms. In this paper, we investigate the impact of compression techniques on FL for a typical image classification task. Going further, we demonstrate that a straightforward method can compresses messages up to 50% while having less than 1% of accuracy loss, competing with state-of-the-art techniques.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

14 of 19 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. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  4. 2020Adaptive Federated Optimizationcited by 1,036
  5. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  6. 2015BinaryConnect: Training Deep Neural Networks with binary weights during propagationscited by 571
  7. 2021Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networkscited by 521
  8. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  9. 2017A Survey of Model Compression and Acceleration for Deep Neural Networkscited by 298
  10. 2022On-Device Training Under 256KB Memorycited by 158
  11. 2020Sparse Weight Activation Trainingcited by 45
  12. 2022ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsitycited by 45
2 more
  1. 2020Rethinking Weight Decay For Efficient Neural Network Pruningcited by 10
  2. 2023AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devicescited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024FLoCoRA: Federated learning compression with low-rank adaptationcited by 3
  2. 2026A Full Compression Pipeline for Green Federated Learning in Communication-Constrained Environmentscited by 0