mlsanityData to 7 Oct 2026

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

arXiv 2109.06710 ↗First posted 14 Sep 2021cs.IT cs.LG math.ITcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 20 of its 26 references that are in the map. First posted on arXiv 14 Sep 2021.

Builds on

20 of 26 references.

References · 20

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradientscited by 557
  3. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  4. 2017TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learningcited by 253
  5. 2017Sparse Communication for Distributed Gradient Descentcited by 243
  6. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  7. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  8. 2019Federated Learning over Wireless Fading Channelscited by 178
  9. 2016QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingcited by 147
  10. 2018ATOMO: Communication-efficient Learning via Atomic Sparsificationcited by 135
  11. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  12. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
8 more
  1. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  2. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  3. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  4. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  5. 2019Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networkscited by 39
  6. 2020Convergence of Federated Learning over a Noisy Downlinkcited by 29
  7. 2021Federated Edge Learning with Misaligned Over-The-Air Computationcited by 28
  8. 2020Communicate to Learn at the Edgecited by 27

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 2; 2024: 2; 2025: 0; 2026: 1.

9 papers.

  1. 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
  2. 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
  3. 2023Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approachcited by 8
  4. 2022Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relayingcited by 5
  5. 2022BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selectioncited by 1
  6. 2024A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learningcited by 1
  7. 2023VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learningcited by 0
  8. 2024Federated Learning with Multi-resolution Model Broadcastcited by 0
  9. 2026Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learningcited by 0