# Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Scheduling

arXiv: https://arxiv.org/abs/2109.06710 · HTML: https://mlsanity.com/paper/2109.06710/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 26 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://mlsanity.com/paper/1606.06160/) (2016)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning](https://mlsanity.com/paper/1705.07878/) (2017)
- [Sparse Communication for Distributed Gradient Descent](https://mlsanity.com/paper/1704.05021/) (2017)
- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding](https://mlsanity.com/paper/1610.02132/) (2016)
- [ATOMO: Communication-efficient Learning via Atomic Sparsification](https://mlsanity.com/paper/1806.04090/) (2018)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [Convergence Time Optimization for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2001.07845/) (2020)
- [Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge](https://mlsanity.com/paper/2001.10402/) (2020)
- [Energy-Efficient Radio Resource Allocation for Federated Edge Learning](https://mlsanity.com/paper/1907.06040/) (2019)
- [Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks](https://mlsanity.com/paper/1910.14648/) (2019)
- [Federated Edge Learning with Misaligned Over-The-Air Computation](https://mlsanity.com/paper/2102.13604/) (2021)
- [Convergence of Federated Learning over a Noisy Downlink](https://mlsanity.com/paper/2008.11141/) (2020)
- [Communicate to Learn at the Edge](https://mlsanity.com/paper/2009.13269/) (2020)

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

- [Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks](https://mlsanity.com/paper/2212.07356/) (2022)
- [Semi-Decentralized Federated Learning with Collaborative Relaying](https://mlsanity.com/paper/2205.10998/) (2022)
- [Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2303.10373/) (2023)
- [Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying](https://mlsanity.com/paper/2202.11850/) (2022)
- [BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selection](https://mlsanity.com/paper/2212.11805/) (2022)
- [A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning](https://mlsanity.com/paper/2410.07881/) (2024)
- [VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning](https://mlsanity.com/paper/2311.18741/) (2023)
- [Federated Learning with Multi-resolution Model Broadcast](https://mlsanity.com/paper/2405.19886/) (2024)
- [Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning](https://mlsanity.com/paper/2607.13119/) (2026)

## Abstract (arXiv)

We consider federated edge learning (FEEL) over wireless fading channels taking into account the downlink and uplink channel latencies, and the random computation delays at the clients. We speed up the training process by overlapping the communication with computation. With fountain coded transmission of the global model update, clients receive the global model asynchronously, and start performing local computations right away. Then, we propose a dynamic client scheduling policy, called MRTP, for uploading local model updates to the parameter server (PS), which, at any time, schedules the client with the minimum remaining upload time. However, MRTP can lead to biased participation of clients in the update process, resulting in performance degradation in non-iid data scenarios. To overcome this, we propose two alternative schemes with fairness considerations, termed as age-aware MRTP (A-MRTP), and opportunistically fair MRTP (OF-MRTP). In A-MRTP, the remaining clients are scheduled according to the ratio between their remaining transmission time and the update age, while in OF-MRTP, the selection mechanism utilizes the long term average channel rate of the clients to further reduce the latency while ensuring fair participation of the clients. It is shown through numerical simulations that OF-MRTP provides significant reduction in latency without sacrificing test accuracy.