mlsanityData to 14 Oct 2026

Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks

arXiv 2012.05137 ↗First posted 9 Dec 2020cs.LG cs.NIcited by 29

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Abstract (arXiv)

With growth in the number of smart devices and advancements in their hardware, in recent years, data-driven machine learning techniques have drawn significant attention. However, due to privacy and communication issues, it is not possible to collect this data at a centralized location. Federated learning is a machine learning setting where the centralized location trains a learning model over remote devices. Federated learning algorithms cannot be employed in the real world scenarios unless they consider unreliable and resource-constrained nature of the wireless medium. In this paper, we propose a federated learning algorithm that is suitable for cellular wireless networks. We prove its convergence, and provide the optimal scheduling policy that maximizes the convergence rate. We also study the effect of local computation steps and communication steps on the convergence of the proposed algorithm. We prove, in practice, federated learning algorithms may solve a different problem than the one that they have been employed for if the unreliability of wireless channels is neglected. Finally, through numerous experiments on real and synthetic datasets, we demonstrate the convergence of our proposed algorithm.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Dec 2020.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 4; 2024: 7; 2025: 6; 2026: 3.

29 papers.

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  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  3. 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
  4. 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
  5. 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
  6. 2022Scalable Hierarchical Over-the-Air Federated Learningcited by 14
  7. 2021Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systemscited by 12
  8. 2022Time-triggered Federated Learning over Wireless Networkscited by 10
  9. 2023Federated Learning While Providing Model as a Service: Joint Training and Inference Optimizationcited by 8
  10. 2021Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learningcited by 7
  11. 2024Decentralized Federated Learning Over Imperfect Communication Channelscited by 5
  12. 2024DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networkscited by 5
17 more
  1. 2025Biased Federated Learning under Wireless Heterogeneitycited by 4
  2. 2022Characterization of the Global Bias Problem in Aerial Federated Learningcited by 3
  3. 2023Dynamic Scheduling for Federated Edge Learning with Streaming Datacited by 3
  4. 2024UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysiscited by 2
  5. 2025Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networkscited by 2
  6. 2023Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysiscited by 1
  7. 2024Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systemscited by 1
  8. 2025Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learningcited by 1
  9. 2024Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clientscited by 0
  10. 2024Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networkscited by 0
  11. 2024Federated Learning with Multi-resolution Model Broadcastcited by 0
  12. 2025Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approachcited by 0
  13. 2025Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learningcited by 0
  14. 2025Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?cited by 0
  15. 2026VR-VFL: Joint Rate and Client Selection for Vehicular Federated Learning Under Imperfect CSIcited by 0
  16. 2026Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learningcited by 0
  17. 2026Polar Code Based Federated Learning: Convergence Analysis and Resource Allocationcited by 0