mlsanityData to 7 Oct 2026

Federated Learning With Quantized Global Model Updates

arXiv 2006.10672 ↗First posted 18 Jun 2020cs.IT cs.DC cs.LGcited by 54

Not on the map. Why?

Abstract (arXiv)

We study federated learning (FL), which enables mobile devices to utilize their local datasets to collaboratively train a global model with the help of a central server, while keeping data localized. At each iteration, the server broadcasts the current global model to the devices for local training, and aggregates the local model updates from the devices to update the global model. Previous work on the communication efficiency of FL has mainly focused on the aggregation of model updates from the devices, assuming perfect broadcasting of the global model. In this paper, we instead consider broadcasting a compressed version of the global model. This is to further reduce the communication cost of FL, which can be particularly limited when the global model is to be transmitted over a wireless medium. We introduce a lossy FL (LFL) algorithm, in which both the global model and the local model updates are quantized before being transmitted. We analyze the convergence behavior of the proposed LFL algorithm assuming the availability of accurate local model updates at the server. Numerical experiments show that the proposed LFL scheme, which quantizes the global model update (with respect to the global model estimate at the devices) rather than the global model itself, significantly outperforms other existing schemes studying quantization of the global model at the PS-to-device direction. Also, the performance loss of the proposed scheme is marginal compared to the fully lossless approach, where the PS and the devices transmit their messages entirely without any quantization.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jun 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 13; 2023: 8; 2024: 9; 2025: 8; 2026: 2.

54 papers.

Sort:

  1. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  2. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 50
  3. 2022ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsitycited by 45
  4. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
  5. 2021DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learningcited by 40
  6. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  7. 2023Personalized Federated Learning under Mixture of Distributionscited by 37
  8. 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 25
  9. 2021Learned Gradient Compression for Distributed Deep Learningcited by 20
  10. 2022FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learningcited by 17
  11. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Designcited by 15
  12. 2021Federated mmWave Beam Selection Utilizing LIDAR Datacited by 12
38 more
  1. 2022Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devicescited by 12
  2. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  3. 2023Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregationcited by 10
  4. 2021On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSIcited by 9
  5. 2021An Expectation-Maximization Perspective on Federated Learningcited by 9
  6. 2023Towards Practical Federated Causal Structure Learningcited by 9
  7. 2021Federated Learning over Noisy Channels: Convergence Analysis and Design Examplescited by 8
  8. 2024Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networkscited by 7
  9. 2022Personalized Federated Learning with Server-Side Informationcited by 6
  10. 2022Personalized Federated Learning with Communication Compressioncited by 6
  11. 2021Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligentcited by 5
  12. 2025Experimentally validated quantum-secure federated learning over a multi-user quantum networkcited by 5
  13. 2022A review of Federated Learning in Intrusion Detection Systems for IoTcited by 4
  14. 2022FedorAS: Federated Architecture Search under system heterogeneitycited by 4
  15. 2022Reconciling Security and Communication Efficiency in Federated Learningcited by 4
  16. 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
  17. 2021Federated Learning with Downlink Device Selectioncited by 3
  18. 2022A-LAQ: Adaptive Lazily Aggregated Quantized Gradientcited by 3
  19. 2024Rate-Constrained Quantization for Communication-Efficient Federated Learningcited by 3
  20. 2021Server Averaging for Federated Learningcited by 2
  21. 2023Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernelcited by 2
  22. 2023How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channelscited by 2
  23. 2023Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsificationcited by 2
  24. 2024Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidthcited by 2
  25. 2025Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networkscited by 2
  26. 2025FedFetch: Faster Federated Learning with Adaptive Downstream Prefetchingcited by 2
  27. 2023QMGeo: Differentially Private Federated Learning via Stochastic Quantization with Mixed Truncated Geometric Distributioncited by 1
  28. 2025FedSAUC: A Similarity-Aware Update Control for Communication-Efficient Federated Learning in Edge Computingcited by 1
  29. 2025γ-FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learningcited by 1
  30. 2025FedBiF: Communication-Efficient Federated Learning via Bits Freezingcited by 1
  31. 2022Encoded Gradients Aggregation against Gradient Leakage in Federated Learningcited by 0
  32. 2022Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneitycited by 0
  33. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 0
  34. 2023FedAgg: Adaptive Federated Learning with Aggregated Gradientscited by 0
  35. 2024Federated Computing -- Survey on Building Blocks, Extensions and Systemscited by 0
  36. 2024An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learningcited by 0
  37. 2024CELLM: An Efficient Communication in Large Language Models Training for Federated Learningcited by 0
  38. 2024A Robust Federated Learning Framework for Undependable Devices at Scalecited by 0

50 most cited of 54 All 54 (JSON)