mlsanityData to 14 Oct 2026

Federated Learning via Plurality Vote

arXiv 2110.02998 ↗First posted 6 Oct 2021cs.LG cs.AI cs.DCcited by 6

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Abstract (arXiv)

Federated learning allows collaborative workers to solve a machine learning problem while preserving data privacy. Recent studies have tackled various challenges in federated learning, but the joint optimization of communication overhead, learning reliability, and deployment efficiency is still an open problem. To this end, we propose a new scheme named federated learning via plurality vote (FedVote). In each communication round of FedVote, workers transmit binary or ternary weights to the server with low communication overhead. The model parameters are aggregated via weighted voting to enhance the resilience against Byzantine attacks. When deployed for inference, the model with binary or ternary weights is resource-friendly to edge devices. We show that our proposed method can reduce quantization error and converges faster compared with the methods directly quantizing the model updates.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 28 of its 49 references that are in the map. First posted on arXiv 6 Oct 2021.

Builds on

28 of 49 references.

References · 28

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  6. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  7. 2018LEAF: A Benchmark for Federated Settingscited by 792
  8. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  9. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  10. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 571
  11. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  12. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
16 more
  1. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
  2. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 337
  3. 2019Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networkscited by 238
  4. 2020Binary Neural Networks: A Surveycited by 190
  5. 2019Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computationscited by 181
  6. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 179
  7. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  8. 2020Federated Learning with Compression: Unified Analysis and Sharp Guaranteescited by 160
  9. 2018Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithmscited by 106
  10. 2019Reliable Federated Learning for Mobile Networkscited by 92
  11. 2019Byzantine-Robust Federated Machine Learning through Adaptive Model Averagingcited by 70
  12. 2019Stochastic Sign Descent Methods: New Algorithms and Better Theorycited by 49
  13. 2020Stochastic-Sign SGD for Federated Learning with Theoretical Guaranteescited by 35
  14. 2017Learning Discrete Weights Using the Local Reparameterization Trickcited by 29
  15. 2019Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithmscited by 29
  16. 2021FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysiscited by 13

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Cited by year

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

6 papers.

  1. 2023Communication-Efficient Split Learning via Adaptive Feature-Wise Compressioncited by 18
  2. 2022Maximizing Communication Efficiency for Large-scale Training via 0/1 Adamcited by 15
  3. 2023New Bounds on the Accuracy of Majority Voting for Multi-Class Classificationcited by 5
  4. 2024FedQV: Leveraging Quadratic Voting in Federated Learningcited by 3
  5. 2025Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontierscited by 3
  6. 2024SignSGD with Federated Votingcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence
  2. 2022Reconciling Security and Communication Efficiency in Federated Learning
  3. 2025Quantized Rank Reduction: A Communications-Efficient Federated Learning Scheme for Network-Critical Applications
  4. 2026Quantization in Federated Learning: Methods, Challenges and Future Directions
  5. 2024FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization
  6. 2023Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity
  7. 2024Byzantine-Robust Federated Learning: An Overview With Focus on Developing Sybil-based Attacks to Backdoor Augmented Secure Aggregation Protocols
  8. 2025FedBiF: Communication-Efficient Federated Learning via Bits Freezing