mlsanityData to 7 Oct 2026

Quantized Federated Learning under Transmission Delay and Outage Constraints

arXiv 2106.09397 ↗First posted 17 Jun 2021cs.IT cs.LG math.ITcited by 37

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge while protecting user privacy. Although various communication schemes have been proposed to expedite the FL process, most of them have assumed ideal wireless channels which provide reliable and lossless communication links between the server and mobile clients. Unfortunately, in practical systems with limited radio resources such as constraint on the training latency and constraints on the transmission power and bandwidth, transmission of a large number of model parameters inevitably suffers from quantization errors (QE) and transmission outage (TO). In this paper, we consider such non-ideal wireless channels, and carry out the first analysis showing that the FL convergence can be severely jeopardized by TO and QE, but intriguingly can be alleviated if the clients have uniform outage probabilities. These insightful results motivate us to propose a robust FL scheme, named FedTOE, which performs joint allocation of wireless resources and quantization bits across the clients to minimize the QE while making the clients have the same TO probability. Extensive experimental results are presented to show the superior performance of FedTOE for deep learning-based classification tasks with transmission latency constraints.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 25 of its 36 references that are in the map. First posted on arXiv 17 Jun 2021.

Builds on

25 of 36 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  5. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  6. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 496
  7. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  8. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  9. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  10. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  11. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  12. 2018Federated Learning via Over-the-Air Computationcited by 215
13 more
  1. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  2. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  3. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  4. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 94
  5. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  6. 2019Variance Reduced Local SGD with Lower Communication Complexitycited by 80
  7. 2020Federated Learning With Quantized Global Model Updatescited by 54
  8. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  9. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
  10. 2020Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networkscited by 29
  11. 2021Distributed Learning Systems with First-order Methodscited by 17
  12. 2021Edge Federated Learning Via Unit-Modulus Over-The-Air Computationcited by 16
  13. 2020Delay Minimization for Federated Learning Over Wireless Communication Networkscited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 10; 2024: 6; 2025: 12; 2026: 4.

37 papers.

Sort:

  1. 2023Why Batch Normalization Damage Federated Learning on Non-IID Data?cited by 29
  2. 2023z-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learningcited by 17
  3. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Designcited by 15
  4. 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computingcited by 15
  5. 2021Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneitycited by 12
  6. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  7. 2023G2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clusteringcited by 9
  8. 2023Making Batch Normalization Great in Federated Deep Learningcited by 8
  9. 2023A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regretcited by 7
  10. 2024Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networkscited by 7
  11. 2022Federated Stochastic Primal-dual Learning with Differential Privacycited by 5
  12. 2022Time Minimization in Hierarchical Federated Learningcited by 4
25 more
  1. 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selectioncited by 4
  2. 2025Biased Federated Learning under Wireless Heterogeneitycited by 4
  3. 2023Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacycited by 3
  4. 2023Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learningcited by 3
  5. 2024Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Designcited by 3
  6. 2023Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsificationcited by 2
  7. 2025Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networkscited by 2
  8. 2025Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learningcited by 2
  9. 2022Communication-Efficient {Federated} Learning Using Censored Heavy Ball Descentcited by 1
  10. 2024Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Designcited by 1
  11. 2025Deploying Large AI Models on Resource-Limited Devices with Split Federated Learningcited by 1
  12. 2025Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learningcited by 1
  13. 2025OLALa: Online Learned Adaptive Lattice Codes for Heterogeneous Federated Learningcited by 1
  14. 2024Digital versus Analog Transmissions for Federated Learning over Wireless Networkscited by 0
  15. 2024A Hierarchical Federated Learning Approach for the Internet of Thingscited by 0
  16. 2025Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approachcited by 0
  17. 2025A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Controlcited by 0
  18. 2025Privacy-Preserving Quantized Federated Learning with Diverse Precisioncited by 0
  19. 2025Lightweight Federated Learning over Wireless Edge Networkscited by 0
  20. 2025Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Designcited by 0
  21. 2025FedLAM: Low-latency Wireless Federated Learning via Layer-wise Adaptive Modulationcited by 0
  22. 2026Coding-Enforced Resilient and Secure Aggregation for Hierarchical Federated Learningcited by 0
  23. 2026Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learningcited by 0
  24. 2026Byzantine-Robust Distributed SGD: A Unified Analysis and Tight Error Boundscited by 0
  25. 2026Polar Code Based Federated Learning: Convergence Analysis and Resource Allocationcited by 0