# Service Delay Minimization for Federated Learning over Mobile Devices

arXiv: https://arxiv.org/abs/2205.09868 · HTML: https://mlsanity.com/paper/2205.09868/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 52 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 26 of 26)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Optimization Methods for Large-Scale Machine Learning](https://mlsanity.com/paper/1606.04838/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [signSGD: Compressed Optimisation for Non-Convex Problems](https://mlsanity.com/paper/1802.04434/) (2018)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks](https://mlsanity.com/paper/1807.10029/) (2018)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations](https://mlsanity.com/paper/1906.02367/) (2019)
- [QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding](https://mlsanity.com/paper/1610.02132/) (2016)
- [Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning](https://mlsanity.com/paper/2007.07174/) (2020)
- [Training Quantized Nets: A Deeper Understanding](https://mlsanity.com/paper/1706.02379/) (2017)
- [Cost-Effective Federated Learning Design](https://mlsanity.com/paper/2012.08336/) (2020)
- [To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices](https://mlsanity.com/paper/2012.11804/) (2020)
- [Cell-Free Massive MIMO for Wireless Federated Learning](https://mlsanity.com/paper/1909.12567/) (2019)
- [Convergence Analysis and System Design for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2105.00872/) (2021)
- [FedDQ: Communication-Efficient Federated Learning with Descending Quantization](https://mlsanity.com/paper/2110.02291/) (2021)
- [CPT: Efficient Deep Neural Network Training via Cyclic Precision](https://mlsanity.com/paper/2101.09868/) (2021)
- [Towards Energy Efficient Federated Learning over 5G+ Mobile Devices](https://mlsanity.com/paper/2101.04866/) (2021)
- [Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach](https://mlsanity.com/paper/2102.07972/) (2021)
- [A flexible framework for communication-efficient machine learning: from HPC to IoT](https://mlsanity.com/paper/2003.06377/) (2020)

## Built on by (most cited first, 9 of 9)

- [Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks](https://mlsanity.com/paper/2303.15991/) (2023)
- [Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks](https://mlsanity.com/paper/2304.08996/) (2023)
- [COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms](https://mlsanity.com/paper/2410.19375/) (2024)
- [Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing](https://mlsanity.com/paper/2405.18707/) (2024)
- [Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning](https://mlsanity.com/paper/2504.09114/) (2025)
- [Efficient Split Federated Learning for Large Language Models over Communication Networks](https://mlsanity.com/paper/2504.14667/) (2025)
- [REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices](https://mlsanity.com/paper/2309.13643/) (2023)
- [Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning](https://mlsanity.com/paper/2311.02130/) (2023)
- [Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learning](https://mlsanity.com/paper/2601.22669/) (2026)

## Abstract (arXiv)

Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices. Unlike traditional communication efficient FL, which regards wireless communications as the bottleneck, we find that under many situations, the local computing delay is comparable to the communication delay during the FL training process, given the development of high-speed wireless transmission techniques. Thus, the service delay in FL should be computing delay + communication delay over training rounds. To minimize the service delay of FL, simply reducing local computing/communication delay independently is not enough. The delay trade-off between local computing and wireless communications must be considered. Besides, we empirically study the impacts of local computing control and compression strategies (i.e., the number of local updates, weight quantization, and gradient quantization) on computing, communication and service delays. Based on those trade-off observation and empirical studies, we develop an optimization scheme to minimize the service delay of FL over heterogeneous devices. We establish testbeds and conduct extensive emulations/experiments to verify our theoretical analysis. The results show that SDEFL reduces notable service delay with a small accuracy drop compared to peer designs.