mlsanityData to 7 Oct 2026

Federated Learning over Wireless IoT Networks with Optimized Communication and Resources

arXiv 2110.11775 ↗First posted 22 Oct 2021cs.LG cs.DC eess.SPcited by 13

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Abstract (arXiv)

To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique especially for large-scale model training. Federated learning (FL), as a paradigm of collaborative learning techniques, has obtained increasing research attention with the benefits of communication efficiency and improved data privacy. Due to the lossy communication channels and limited communication resources (e.g., bandwidth and power), it is of interest to investigate fast responding and accurate FL schemes over wireless systems. Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of things (IoT) networks. To reduce complexity, we divide the overall optimization problem into two sub-problems, i.e., the client scheduling problem and the resource allocation problem. To reduce the communication costs for FL in wireless IoT networks, a new client scheduling policy is proposed by reusing stale local model parameters. To maximize successful information exchange over networks, a Lagrange multiplier method is first leveraged by decoupling variables including power variables, bandwidth variables and transmission indicators. Then a linear-search based power and bandwidth allocation method is developed. Given appropriate hyper-parameters, we show that the proposed communication-efficient federated learning (CEFL) framework converges at a strong linear rate. Through extensive experiments, it is revealed that the proposed CEFL framework substantially boosts both the communication efficiency and learning performance of both training loss and test accuracy for FL over wireless IoT networks compared to a basic FL approach with uniform resource allocation.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 33 of its 54 references that are in the map. First posted on arXiv 22 Oct 2021.

Builds on

33 of 54 references.

References · 33

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2018Federated Learning with Non-IID Datacited by 1,099
  4. 2019Towards Federated Learning at Scale: System Designcited by 826
  5. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  6. 2020Federated Learning with Matched Averagingcited by 564
  7. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  8. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 471
  9. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  10. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  11. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  12. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 303
21 more
  1. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  2. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  3. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  4. 2019Federated Learning over Wireless Fading Channelscited by 178
  5. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  6. 2018LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learningcited by 95
  7. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 94
  8. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
  9. 2019Reliable Federated Learning for Mobile Networkscited by 92
  10. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  11. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  12. 2020Multi-Armed Bandit Based Client Scheduling for Federated Learningcited by 56
  13. 2017Communication-Efficient Algorithms for Decentralized and Stochastic Optimizationcited by 42
  14. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  15. 2020Federated Learning for 6G Communications: Challenges, Methods, and Future Directionscited by 32
  16. 2020Coded Computing for Low-Latency Federated Learning over Wireless Edge Networkscited by 32
  17. 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 24
  18. 2020Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networkscited by 11
  19. 2020Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computingcited by 5
  20. 2021Federated Learning with Downlink Device Selectioncited by 3
  21. 2017Wireless Powered Dense Cellular Networks: How Many Small Cells Do We Need?cited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 1; 2024: 2; 2025: 3; 2026: 2.

13 papers.

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  1. 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
  2. 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
  3. 2025Biased Federated Learning under Wireless Heterogeneitycited by 4
  4. 2023Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Thingscited by 3
  5. 2025Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learningcited by 3
  6. 2022Resource Allocation for Compression-aided Federated Learning with High Distortion Ratecited by 2
  7. 2022On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stationscited by 2
  8. 2022Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learningcited by 1
  9. 2025FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retrainingcited by 1
  10. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 0
  11. 2024Federated Learning with Multi-resolution Model Broadcastcited by 0
  12. 2026Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learningcited by 0
1 more
  1. 2026Polar Code Based Federated Learning: Convergence Analysis and Resource Allocationcited by 0