A Demonstration of Over-the-Air Computation for Federated Edge Learning

• URL: http://arxiv.org/abs/2209.09954v1
• Date: Tue, 20 Sep 2022 19:08:49 GMT
• Title: A Demonstration of Over-the-Air Computation for Federated Edge Learning
• Authors: Alphan Sahin
• Abstract summary: The proposed method relies on the detection of a synchronization waveform in both receive and transmit directions. By implementing this synchronization method on a set of low-cost SDRs, we demonstrate the performance of frequency-shift keying (FSK)-based majority vote (MV)
• Score: 8.22379888383833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we propose a general-purpose synchronization method that allows a set of software-defined radios (SDRs) to transmit or receive any in-phase/quadrature data with precise timings while maintaining the baseband processing in the corresponding companion computers. The proposed method relies on the detection of a synchronization waveform in both receive and transmit directions and controlling the direct memory access blocks jointly with the processing system. By implementing this synchronization method on a set of low-cost SDRs, we demonstrate the performance of frequency-shift keying (FSK)-based majority vote (MV), i.e., an over-the-air computation scheme for federated edge learning, and introduce the corresponding procedures. Our experiment shows that the test accuracy can reach more than 95% for homogeneous and heterogeneous data distributions without using channel state information at the edge devices.

Related papers

• Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets [53.367212596352324]
  We propose an unsupervised approach leveraging EEG signal physics. We map EEG channels to fixed positions using field, source-free domain adaptation. Our method demonstrates robust performance in brain-computer interface (BCI) tasks and potential biomarker applications.
  arXiv  Detail & Related papers  (2024-03-07T16:17:33Z)
• Robust Fully-Asynchronous Methods for Distributed Training over General Architecture [11.480605289411807]
  Perfect synchronization in distributed machine learning problems is inefficient and even impossible due to the existence of latency, package losses and stragglers. We propose Fully-Asynchronous Gradient Tracking method (R-FAST), where each device performs local computation and communication at its own without any form of impact.
  arXiv  Detail & Related papers  (2023-07-21T14:36:40Z)
• Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments [93.30657979626858]
  We propose a deep actor-critic algorithm that accounts for channel correlations and destination motion. We show that, when channels aretemporally correlated, the inclusion of the SNR in the state representation with function approximation in ways that inhibit convergence.
  arXiv  Detail & Related papers  (2022-11-02T22:07:36Z)
• Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics [77.13542705329328]
  We propose a framework of disentangled representation learning(DRL) that first learns to factor the input signals into a device-relevant component and a device-irrelevant component via adversarial learning. The implicit data augmentation in the proposed framework imposes a regularization on the RFF extractor to avoid the possible overfitting of device-irrelevant channel statistics. Experiments validate that the proposed approach, referred to as DR-RFF, outperforms conventional methods in terms of generalizability to unknown complicated propagation environments.
  arXiv  Detail & Related papers  (2022-08-04T15:46:48Z)
• Data post-processing for the one-way heterodyne protocol under composable finite-size security [62.997667081978825]
  We study the performance of a practical continuous-variable (CV) quantum key distribution protocol. We focus on the Gaussian-modulated coherent-state protocol with heterodyne detection in a high signal-to-noise ratio regime. This allows us to study the performance for practical implementations of the protocol and optimize the parameters connected to the steps above.
  arXiv  Detail & Related papers  (2022-05-20T12:37:09Z)
• Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication [40.84033154889936]
  This letter studies a vertical federated edge learning (FEEL) system for collaborative objects/human motion recognition. In this system, distributed edge devices first send wireless signals to sense targeted objects/human, and then exchange intermediate computed vectors for collaborative recognition. By considering a human motion recognition task, experimental results show that our vertical FEEL based approach achieves recognition accuracy up to 98% with an improvement up to 8% compared to the benchmarks.
  arXiv  Detail & Related papers  (2022-01-21T02:05:07Z)
• VAD-free Streaming Hybrid CTC/Attention ASR for Unsegmented Recording [46.69852287267763]
  We propose a block-synchronous beam search decoding to take advantage of efficient batched output-synchronous and low-latency input-synchronous searches. We also propose a VAD-free inference algorithm that leverages probabilities to determine a suitable timing to reset the model states. Experimental evaluations demonstrate that the block-synchronous decoding achieves comparable accuracy to the label-synchronous one.
  arXiv  Detail & Related papers  (2021-07-15T17:59:10Z)
• Composably secure data processing for Gaussian-modulated continuous variable quantum key distribution [58.720142291102135]
  Continuous-variable quantum key distribution (QKD) employs the quadratures of a bosonic mode to establish a secret key between two remote parties. We consider a protocol with homodyne detection in the general setting of composable finite-size security. In particular, we analyze the high signal-to-noise regime which requires the use of high-rate (non-binary) low-density parity check codes.
  arXiv  Detail & Related papers  (2021-03-30T18:02:55Z)
• Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning [9.179817518536545]
  We study a federated learning system at the wireless edge that uses over-the-air computation (AirComp) In such a system, users transmit their messages over a multi-access channel concurrently to achieve fast model aggregation. We propose an improved digital AirComp scheme to relax its requirements on the transmitters, where users perform phase correction and transmit with full power.
  arXiv  Detail & Related papers  (2020-08-03T16:29:52Z)
• Fine Timing and Frequency Synchronization for MIMO-OFDM: An Extreme Learning Approach [8.432859469083951]
  We propose a novel scheme leveraging extreme learning machine (ELM) to achieve high-precision synchronization. The proposed method is robust in terms of the choice of channel parameters and also in terms of "generalization ability" from a machine learning standpoint.
  arXiv  Detail & Related papers  (2020-07-17T21:41:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.