mlsanityData to 14 Oct 2026

CFLIT: Coexisting Federated Learning and Information Transfer

arXiv 2207.12884 ↗First posted 26 Jul 2022cs.IT cs.LG cs.NIcited by 4

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Abstract (arXiv)

Future wireless networks are expected to support diverse mobile services, including artificial intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a revolutionary learning approach, enables collaborative AI model training across distributed mobile edge devices. By exploiting the superposition property of multiple-access channels, over-the-air computation allows concurrent model uploading from massive devices over the same radio resources, and thus significantly reduces the communication cost of FL. In this paper, we study the coexistence of over-the-air FL and traditional information transfer (IT) in a mobile edge network. We propose a coexisting federated learning and information transfer (CFLIT) communication framework, where the FL and IT devices share the wireless spectrum in an OFDM system. Under this framework, we aim to maximize the IT data rate and guarantee a given FL convergence performance by optimizing the long-term radio resource allocation. A key challenge that limits the spectrum efficiency of the coexisting system lies in the large overhead incurred by frequent communication between the server and edge devices for FL model aggregation. To address the challenge, we rigorously analyze the impact of the computation-to-communication ratio on the convergence of over-the-air FL in wireless fading channels. The analysis reveals the existence of an optimal computation-to-communication ratio that minimizes the amount of radio resources needed for over-the-air FL to converge to a given error tolerance. Based on the analysis, we propose a low-complexity online algorithm to jointly optimize the radio resource allocation for both the FL devices and IT devices. Extensive numerical simulations verify the superior performance of the proposed design for the coexistence of FL and IT devices in wireless cellular systems.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 29 of its 41 references that are in the map. First posted on arXiv 26 Jul 2022.

Builds on

29 of 41 references.

References · 29

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2018Local SGD Converges Fast and Communicates Littlecited by 605
  5. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 571
  6. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
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  8. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  9. 2018Sparsified SGD with Memorycited by 376
  10. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 228
  11. 2018Federated Learning via Over-the-Air Computationcited by 219
  12. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
17 more
  1. 2019Federated Learning over Wireless Fading Channelscited by 183
  2. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 160
  3. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 153
  4. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  5. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  6. 2018Wireless Network Intelligence at the Edgecited by 92
  7. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  8. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  9. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 63
  10. 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 48
  11. 20206G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunitiescited by 35
  12. 2020Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approachcited by 33
  13. 2021Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computationcited by 29
  14. 2020Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Managementcited by 26
  15. 2021Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learningcited by 10
  16. 2021Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validationcited by 10
  17. 2021Relay-Assisted Cooperative Federated Learningcited by 9

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Cited by year

Citing papers per year: 2023: 3; 2024: 1.

4 papers.

  1. 2023Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learningcited by 9
  2. 2023Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Datacited by 7
  3. 2023Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channelscited by 5
  4. 2024How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learningcited by 2

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  1. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
  2. 2025Optimal Transceiver Design in Over-the-Air Federated Distillation
  3. 2023Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
  4. 2026Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks
  5. 2025Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks
  6. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions
  7. 2022Over-the-Air Federated Learning via Second-Order Optimization
  8. 2021Quantized Federated Learning under Transmission Delay and Outage Constraints