mlsanityData to 7 Oct 2026

Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis

arXiv 2104.10095 ↗First posted 20 Apr 2021cs.IT cs.DC cs.LGcited by 13

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Abstract (arXiv)

Recently years, the attempts on distilling mobile data into useful knowledge has been led to the deployment of machine learning algorithms at the network edge. Principal component analysis (PCA) is a classic technique for extracting the linear structure of a dataset, which is useful for feature extraction and data compression. In this work, we propose the deployment of distributed PCA over a multi-access channel based on the algorithm of stochastic gradient descent to learn the dominant feature space of a distributed dataset at multiple devices. Over-the-air aggregation is adopted to reduce the multi-access latency, giving the name over-the-air PCA. The novelty of this design lies in exploiting channel noise to accelerate the descent in the region around each saddle point encountered by gradient descent, thereby increasing the convergence speed of over-the-air PCA. The idea is materialized by proposing a power-control scheme which detects the type of descent region and controlling the level of channel noise accordingly. The scheme is proved to achieve a faster convergence rate than in the case without power control.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 23 of its 42 references that are in the map. First posted on arXiv 20 Apr 2021.

Builds on

23 of 42 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  3. 2017How to Escape Saddle Points Efficientlycited by 284
  4. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  5. 2015Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decompositioncited by 241
  6. 2018Federated Learning via Over-the-Air Computationcited by 215
  7. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  8. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  9. 2019Federated Learning over Wireless Fading Channelscited by 178
  10. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  11. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  12. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 103
11 more
  1. 2020On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problemscited by 79
  2. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  3. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
  4. 2020Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computingcited by 39
  5. 2019High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learningcited by 32
  6. 2020Energy-Efficient Federated Edge Learning with Joint Communication and Computation Designcited by 30
  7. 2014Improved Distributed Principal Component Analysiscited by 14
  8. 2020Hybrid Beamforming for Massive MIMO Over-the-Air Computationcited by 12
  9. 2020Gradient Statistics Aware Power Control for Over-the-Air Federated Learningcited by 5
  10. 2019Federated Principal Component Analysiscited by 4
  11. 2020Federated Over-Air Subspace Tracking from Incomplete and Corrupted Datacited by 2

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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 5; 2024: 4; 2025: 1.

13 papers.

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  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  2. 2022A Survey on Over-the-Air Computationcited by 19
  3. 2022Scalable Hierarchical Over-the-Air Federated Learningcited by 14
  4. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  5. 2023Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interferencecited by 9
  6. 2023Edge Intelligence Over the Air: Two Faces of Interference in Federated Learningcited by 5
  7. 2024Over-the-Air Federated Learning via Weighted Aggregationcited by 5
  8. 2024Adaptive Federated Learning Over the Aircited by 4
  9. 2024Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clippingcited by 3
  10. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processingcited by 3
  11. 2023Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysiscited by 1
  12. 2023Byzantine-Resilient Federated PCA and Low Rank Column-wise Sensingcited by 0
1 more
  1. 2024Generation of Uncorrelated Residual Variables for Chemical Process Fault Diagnosis via Transfer Learning-based Input-Output Decoupled Networkcited by 0