mlsanityData to 14 Oct 2026

Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking

arXiv 2205.13054 ↗First posted 25 May 2022cs.DC cs.LGcited by 18

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Abstract (arXiv)

Federated learning (FL) enables collaborative model training without centralizing data. However, the traditional FL framework is cloud-based and suffers from high communication latency. On the other hand, the edge-based FL framework that relies on an edge server co-located with mobile base station for model aggregation has low communication latency but suffers from degraded model accuracy due to the limited coverage of edge server. In light of high accuracy but high-latency cloud-based FL and low-latency but low-accuracy edge-based FL, this paper proposes a new FL framework based on cooperative mobile edge networking called cooperative federated edge learning (CFEL) to enable both high-accuracy and low-latency distributed intelligence at mobile edge networks. Considering the unique two-tier network architecture of CFEL, a novel federated optimization method dubbed cooperative edge-based federated averaging (CE-FedAvg) is further developed, wherein each edge server both coordinates collaborative model training among the devices within its own coverage and cooperates with other edge servers to learn a shared global model through decentralized consensus. Experimental results based on benchmark datasets show that CFEL can largely reduce the training time to achieve a target model accuracy compared with prior FL frameworks.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 29 of its 46 references that are in the map. First posted on arXiv 25 May 2022.

Builds on

29 of 46 references.

References · 29

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  4. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  5. 2020Adaptive Federated Optimizationcited by 1,046
  6. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  7. 2018LEAF: A Benchmark for Federated Settingscited by 792
  8. 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 367
  9. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  10. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  11. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  12. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
17 more
  1. 2019On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimizationcited by 180
  2. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 179
  3. 2018ATOMO: Communication-efficient Learning via Atomic Sparsificationcited by 136
  4. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  5. 2017Network Topology and Communication-Computation Tradeoffs in Decentralized Optimizationcited by 122
  6. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  7. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 95
  8. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  9. 2019Abnormal Client Behavior Detection in Federated Learningcited by 40
  10. 2020Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computingcited by 39
  11. 2019Accelerating DNN Training in Wireless Federated Edge Learning Systemscited by 34
  12. 2020Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimizationcited by 26
  13. 2020Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDcited by 26
  14. 2021Real-time End-to-End Federated Learning: An Automotive Case Studycited by 15
  15. 2020Hierarchical Federated Learning through LAN-WAN Orchestrationcited by 13
  16. 2021Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Datacited by 12
  17. 2021Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneitycited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 8; 2025: 2; 2026: 2.

18 papers.

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  1. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  2. 2023Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devicescited by 13
  3. 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainabilitycited by 7
  4. 2023Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computingcited by 4
  5. 2023Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysiscited by 4
  6. 2023FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Modelscited by 4
  7. 2024Hierarchical Federated ADMMcited by 4
  8. 2022On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stationscited by 2
  9. 2024Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compressioncited by 2
  10. 2026FedKRSO: Communication and Memory Efficient Federated Fine-Tuning of Large Language Modelscited by 2
  11. 2024Hierarchical Learning and Computing over Space-Ground Integrated Networkscited by 1
  12. 2025Pareto Actor-Critic for Communication and Computation Co-Optimization in Non-Cooperative Federated Learning Servicescited by 1
6 more
  1. 2024A Hierarchical Federated Learning Approach for the Internet of Thingscited by 0
  2. 2024DP2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimizationcited by 0
  3. 2024Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learningcited by 0
  4. 2024Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learningcited by 0
  5. 2025Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodologycited by 0
  6. 2026Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregationcited by 0