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Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)

arXiv 1812.11494 ↗First posted 30 Dec 2018cs.IT cs.LG math.ITcited by 191

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Abstract (arXiv)

The popularity of mobile devices results in the availability of enormous data and computational resources at the network edge. To leverage the data and resources, a new machine learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing fast and intelligent services to mobile users. While computing speeds are advancing rapidly, the communication latency is becoming the bottleneck of fast edge learning. To address this issue, this work is focused on designing a low latency multi-access scheme for edge learning. We consider a popular framework, federated edge learning (FEEL), where edge-server and on-device learning are synchronized to train a model without violating user-data privacy. It is proposed that model updates simultaneously transmitted by devices over broadband channels should be analog aggregated "over-the-air" by exploiting the superposition property of a multi-access channel. Thereby, "interference" is harnessed to provide fast implementation of the model aggregation. This results in dramatical latency reduction compared with the traditional orthogonal access (i.e., OFDMA). In this work, the performance of FEEL is characterized targeting a single-cell random network. First, due to power alignment between devices as required for aggregation, a fundamental tradeoff is shown to exist between the update-reliability and the expected update-truncation ratio. This motivates the design of an opportunistic scheduling scheme for FEEL that selects devices within a distance threshold. This scheme is shown using real datasets to yield satisfactory learning performance in the presence of high mobility. Second, both the multi-access latency of the proposed analog aggregation and the OFDMA scheme are analyzed. Their ratio, which quantifies the latency reduction of the former, is proved to scale almost linearly with device population.

Key facts. Cited by 191 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Dec 2018.

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Citing papers per year: 2021: 55; 2022: 31; 2023: 35; 2024: 30; 2025: 21; 2026: 19.

191 papers.

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  1. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  2. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  3. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  4. 2022Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learningcited by 59
  5. 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
  6. 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
  7. 2021Federated Learning: A Signal Processing Perspectivecited by 49
  8. 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
  9. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  10. 2021Federated Edge Learning with Misaligned Over-The-Air Computationcited by 30
  11. 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 26
  12. 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
38 more
  1. 2021Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregationcited by 22
  2. 2022A Survey on Over-the-Air Computationcited by 21
  3. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  4. 2022Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networkscited by 18
  5. 2022Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networkingcited by 18
  6. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  7. 2021Edge Federated Learning Via Unit-Modulus Over-The-Air Computationcited by 16
  8. 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
  9. 2023On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharingcited by 16
  10. 2023Accelerating Split Federated Learning over Wireless Communication Networkscited by 16
  11. 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
  12. 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systemscited by 15
  13. 2021Joint Optimization of Communications and Federated Learning Over the Aircited by 14
  14. 2021Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysiscited by 14
  15. 2021Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspectivecited by 14
  16. 2021Accelerated Gradient Descent Learning over Multiple Access Fading Channelscited by 14
  17. 2022Task-Oriented Over-the-Air Computation for Multi-Device Edge AIcited by 14
  18. 2022Scalable Hierarchical Over-the-Air Federated Learningcited by 14
  19. 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14
  20. 2021Efficient Federated Meta-Learning over Multi-Access Wireless Networkscited by 13
  21. 2021Federated Learning over Wireless IoT Networks with Optimized Communication and Resourcescited by 13
  22. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  23. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  24. 2024A Survey on Integrated Sensing, Communication, and Computationcited by 12
  25. 2021Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approachcited by 11
  26. 20211-Bit Compressive Sensing for Efficient Federated Learning Over the Aircited by 11
  27. 2021Hierarchical Over-the-Air Federated Edge Learningcited by 11
  28. 2022Over-the-Air Federated Learning via Second-Order Optimizationcited by 11
  29. 2021Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networkscited by 10
  30. 2021Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfercited by 10
  31. 2021Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learningcited by 10
  32. 2022Over-the-Air Ensemble Inference with Model Privacycited by 10
  33. 2022Over-the-Air Federated Edge Learning with Hierarchical Clusteringcited by 10
  34. 2022Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learningcited by 10
  35. 2023Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Predictioncited by 10
  36. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  37. 2025Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Frameworkcited by 10
  38. 2021Relay-Assisted Cooperative Federated Learningcited by 9

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