# Quantized Federated Learning under Transmission Delay and Outage Constraints

arXiv: https://arxiv.org/abs/2106.09397 · HTML: https://mlsanity.com/paper/2106.09397/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning](https://mlsanity.com/paper/1807.06629/) (2018)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018)
- [One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis](https://mlsanity.com/paper/2001.05713/) (2020)
- [Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective](https://mlsanity.com/paper/2004.04314/) (2020)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [Towards an Intelligent Edge: Wireless Communication Meets Machine Learning](https://mlsanity.com/paper/1809.00343/) (2018)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [Variance Reduced Local SGD with Lower Communication Complexity](https://mlsanity.com/paper/1912.12844/) (2019)
- [Federated Learning With Quantized Global Model Updates](https://mlsanity.com/paper/2006.10672/) (2020)
- [Design and Analysis of Uplink and Downlink Communications for Federated Learning](https://mlsanity.com/paper/2012.04057/) (2020)
- [Device Scheduling with Fast Convergence for Wireless Federated Learning](https://mlsanity.com/paper/1911.00856/) (2019)
- [Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks](https://mlsanity.com/paper/2012.05137/) (2020)
- [Distributed Learning Systems with First-order Methods](https://mlsanity.com/paper/2104.05245/) (2021)
- [Edge Federated Learning Via Unit-Modulus Over-The-Air Computation](https://mlsanity.com/paper/2101.12051/) (2021)
- [Delay Minimization for Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/2007.03462/) (2020)

## Built on by (most cited first, 30 of 37)

- [Why Batch Normalization Damage Federated Learning on Non-IID Data?](https://mlsanity.com/paper/2301.02982/) (2023)
- [z-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning](https://mlsanity.com/paper/2302.02589/) (2023)
- [Wireless Quantized Federated Learning: A Joint Computation and Communication Design](https://mlsanity.com/paper/2203.05878/) (2022)
- [Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing](https://mlsanity.com/paper/2407.08462/) (2024)
- [Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity](https://mlsanity.com/paper/2112.10313/) (2021)
- [Over-the-Air Federated Learning and Optimization](https://mlsanity.com/paper/2310.10089/) (2023)
- [G2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering](https://mlsanity.com/paper/2306.04984/) (2023)
- [Making Batch Normalization Great in Federated Deep Learning](https://mlsanity.com/paper/2303.06530/) (2023)
- [A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret](https://mlsanity.com/paper/2301.08869/) (2023)
- [Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks](https://mlsanity.com/paper/2412.06414/) (2024)
- [Federated Stochastic Primal-dual Learning with Differential Privacy](https://mlsanity.com/paper/2204.12284/) (2022)
- [Time Minimization in Hierarchical Federated Learning](https://mlsanity.com/paper/2210.04689/) (2022)
- [Asynchronous Wireless Federated Learning with Probabilistic Client Selection](https://mlsanity.com/paper/2311.16741/) (2023)
- [Biased Federated Learning under Wireless Heterogeneity](https://mlsanity.com/paper/2503.06078/) (2025)
- [Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy](https://mlsanity.com/paper/2304.04164/) (2023)
- [Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning](https://mlsanity.com/paper/2306.02990/) (2023)
- [Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design](https://mlsanity.com/paper/2407.13703/) (2024)
- [Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification](https://mlsanity.com/paper/2310.19558/) (2023)
- [Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks](https://mlsanity.com/paper/2501.11267/) (2025)
- [Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning](https://mlsanity.com/paper/2505.11570/) (2025)
- [Communication-Efficient {Federated} Learning Using Censored Heavy Ball Descent](https://mlsanity.com/paper/2209.11944/) (2022)
- [Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design](https://mlsanity.com/paper/2410.05662/) (2024)
- [Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning](https://mlsanity.com/paper/2504.09114/) (2025)
- [Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning](https://mlsanity.com/paper/2505.11304/) (2025)
- [OLALa: Online Learned Adaptive Lattice Codes for Heterogeneous Federated Learning](https://mlsanity.com/paper/2506.20297/) (2025)
- [Digital versus Analog Transmissions for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2402.09657/) (2024)
- [A Hierarchical Federated Learning Approach for the Internet of Things](https://mlsanity.com/paper/2403.01540/) (2024)
- [Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach](https://mlsanity.com/paper/2502.17260/) (2025)
- [A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control](https://mlsanity.com/paper/2504.01752/) (2025)
- [Privacy-Preserving Quantized Federated Learning with Diverse Precision](https://mlsanity.com/paper/2507.00920/) (2025)

## 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.