mlsanityData to 14 Oct 2026

Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission

arXiv 2206.01373 ↗First posted 3 Jun 2022eess.SP cs.IT cs.LGcited by 1

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Abstract (arXiv)

This work studies federated learning (FL) over a fog radio access network, in which multiple internet-of-things (IoT) devices cooperatively learn a shared machine learning model by communicating with a cloud server (CS) through distributed access points (APs). Under the assumption that the fronthaul links connecting APs to CS have finite capacity, a rate-splitting transmission at IoT devices (IDs) is proposed which enables hybrid edge and cloud decoding of split uplink messages. The problem of completion time minimization for FL is tackled by optimizing the rate-splitting transmission and fronthaul quantization strategies along with training hyperparameters such as precision and iteration numbers. Numerical results show that the proposed rate-splitting transmission achieves notable gains over benchmark schemes which rely solely on edge or cloud decoding.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 3 Jun 2022.

Builds on

8 of 16 references.

References · 8

  1. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  2. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  3. 2018Wireless Network Intelligence at the Edgecited by 92
  4. 2020Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applicationscited by 62
  5. 2019Cell-Free Massive MIMO for Wireless Federated Learningcited by 25
  6. 2022Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trendscited by 11
  7. 2021Collaborative Cloud and Edge Mobile Computing in C-RAN Systems with Minimal End-to-End Latencycited by 3
  8. 2021Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networkscited by 3

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3

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  1. 2024Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks
  2. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization
  3. 2023Green Federated Learning Over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks
  4. 2025Lightweight Federated Learning over Wireless Edge Networks
  5. 2024FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
  6. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
  7. 2026Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks
  8. 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling