mlsanityData to 14 Oct 2026

Communication-Efficient Federated Learning with Binary Neural Networks

arXiv 2110.02226 ↗First posted 5 Oct 2021cs.LGcited by 14

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) is a privacy-preserving machine learning setting that enables many devices to jointly train a shared global model without the need to reveal their data to a central server. However, FL involves a frequent exchange of the parameters between all the clients and the server that coordinates the training. This introduces extensive communication overhead, which can be a major bottleneck in FL with limited communication links. In this paper, we consider training the binary neural networks (BNN) in the FL setting instead of the typical real-valued neural networks to fulfill the stringent delay and efficiency requirement in wireless edge networks. We introduce a novel FL framework of training BNN, where the clients only upload the binary parameters to the server. We also propose a novel parameter updating scheme based on the Maximum Likelihood (ML) estimation that preserves the performance of the BNN even without the availability of aggregated real-valued auxiliary parameters that are usually needed during the training of the BNN. Moreover, for the first time in the literature, we theoretically derive the conditions under which the training of BNN is converging. { Numerical results show that the proposed FL framework significantly reduces the communication cost compared to the conventional neural networks with typical real-valued parameters, and the performance loss incurred by the binarization can be further compensated by a hybrid method.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 21 of its 28 references that are in the map. First posted on arXiv 5 Oct 2021.

Builds on

21 of 28 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 849
  6. 2018LEAF: A Benchmark for Federated Settingscited by 792
  7. 2015BinaryConnect: Training Deep Neural Networks with binary weights during propagationscited by 592
  8. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  9. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  10. 2017Sparse Communication for Distributed Gradient Descentcited by 249
  11. 2016Ternary Weight Networkscited by 204
  12. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 195
9 more
  1. 2016Distributed Mean Estimation with Limited Communicationcited by 136
  2. 2019Natural Compression for Distributed Deep Learningcited by 90
  3. 2020Ternary Compression for Communication-Efficient Federated Learningcited by 57
  4. 2015Communication Lower Bounds for Statistical Estimation Problems via a Distributed Data Processing Inequalitycited by 50
  5. 2016Randomized Distributed Mean Estimation: Accuracy vs Communicationcited by 39
  6. 2018Geometric Lower Bounds for Distributed Parameter Estimation under Communication Constraintscited by 33
  7. 2018Inference under Information Constraints I: Lower Bounds from Chi-Square Contractioncited by 31
  8. 2019Gradient Descent with Compressed Iteratescited by 11
  9. 2019Lower Bounds for Learning Distributions under Communication Constraints via Fisher Informationcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 4; 2024: 2; 2025: 2; 2026: 3.

14 papers.

Sort:

  1. 2023Green Edge AI: A Contemporary Surveycited by 25
  2. 2022Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Designcited by 15
  3. 2021On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networkscited by 10
  4. 2024FedBAT: Communication-Efficient Federated Learning via Learnable Binarizationcited by 9
  5. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  6. 2021How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learningcited by 6
  7. 2025The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learningcited by 5
  8. 2023Binary Federated Learning with Client-Level Differential Privacycited by 2
  9. 2024Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantizationcited by 1
  10. 2023CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learningcited by 0
  11. 2025P2U: Progressive Precision Update For Efficient Model Distributioncited by 0
  12. 2026Federated Learning of Binary Neural Networks: Enabling Low-Cost Inferencecited by 0
2 more
  1. 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networkscited by 0
  2. 2026FedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantizationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Federated Learning with Downlink Device Selection
  2. 2024FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
  3. 2022Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
  4. 2025Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks
  5. 2025Lightweight Federated Learning over Wireless Edge Networks
  6. 2022CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
  7. 2025Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization
  8. 2022Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing