Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregation

• URL: http://arxiv.org/abs/2602.02469v1
• Date: Mon, 02 Feb 2026 18:50:51 GMT
• Title: Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregation
• Authors: Ahmed M. Elshazly, Ahmed Arafa,
• Abstract summary: We study federated learning over wireless fading channels where multiple devices simultaneously send their model updates.<n>We propose an efficient emphage-aware edge-blind over-the-air FL approach that does not require channel state information at the devices.
• Score: 2.750124853532831
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study federated learning (FL) over wireless fading channels where multiple devices simultaneously send their model updates. We propose an efficient \emph{age-aware edge-blind over-the-air FL} approach that does not require channel state information (CSI) at the devices. Instead, the parameter server (PS) uses multiple antennas and applies maximum-ratio combining (MRC) based on its estimated sum of the channel gains to detect the parameter updates. A key challenge is that the number of orthogonal subcarriers is limited; thus, transmitting many parameters requires multiple Orthogonal Frequency Division Multiplexing (OFDM) symbols, which increases latency. To address this, the PS selects only a small subset of model coordinates each round using \emph{AgeTop-\(k\)}, which first picks the largest-magnitude entries and then chooses the \(k\) coordinates with the longest waiting times since they were last selected. This ensures that all selected parameters fit into a single OFDM symbol, reducing latency. We provide a convergence bound that highlights the advantages of using a higher number of antenna array elements and demonstrates a key trade-off: increasing \(k\) decreases compression error at the cost of increasing the effect of channel noise. Experimental results show that (i) more PS antennas greatly improve accuracy and convergence speed; (ii) AgeTop-\(k\) outperforms random selection under relatively good channel conditions; and (iii) the optimum \(k\) depends on the channel, with smaller \(k\) being better in noisy settings.

Related papers

• Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based? [89.05848771674773]
  A novel antenna system ()-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed.<n>It consists of multiple waveguides, which equip numerous low-cost antennas, named (PAs)<n>The positions of PAs can be reconfigured to both spanning large-scale path and space.
  arXiv  Detail & Related papers  (2025-02-12T18:54:10Z)
• HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers [5.402030962296633]
  Holographic Metasurface Transceivers (HMTs) are emerging as cost-effective substitutes to large antenna arrays for beamforming in Millimeter and TeraHertz wave communication. To achieve desired channel gains through beamforming in HMT, phase-shifts of a large number of elements need to be appropriately set, which is challenging. We develop a learning algorithm using a it fixed-budget multi-armed bandit framework to beamform and maximize received signal strength at the receiver for far-field regions.
  arXiv  Detail & Related papers  (2023-12-30T03:29:32Z)
• DEFORM: A Practical, Universal Deep Beamforming System [4.450750414447688]
  We introduce, design, and evaluate a set of universal receiver beamforming techniques. Our approach and system DEFORM, a Deep Learning (DL) based RX beamforming achieves significant gain for multi antenna RF receivers.
  arXiv  Detail & Related papers  (2022-03-18T03:52:18Z)
• FOVEA: Foveated Image Magnification for Autonomous Navigation [53.69803081925454]
  We propose an attentional approach that elastically magnifies certain regions while maintaining a small input canvas. Our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning. On the autonomous driving datasets Argoverse-HD and BDD100K, we show our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning.
  arXiv  Detail & Related papers  (2021-08-27T03:07:55Z)
• Two-step Machine Learning Approach for Channel Estimation with Mixed Resolution RF Chains [19.0581196881206]
  We propose an efficient uplink channel estimator by applying machine learning (ML) algorithms. In a first step a conditional generative adversarial network (cGAN) predicts the radio channels from a limited set of full resolution RF chains to the rest of the low resolution RF chain antenna elements. A long-short term memory (LSTM) neural network extracts further phase information from the low resolution RF chain antenna elements.
  arXiv  Detail & Related papers  (2021-01-24T12:33:54Z)
• Rate Region for Indirect Multiterminal Source Coding in Federated Learning [49.574683687858126]
  A large number of edge devices send their updates to the edge at each round of the local model. Existing works do not leverage the focus in the information transmitted by different edges. This paper studies the rate region for the indirect multiterminal source coding FL.
  arXiv  Detail & Related papers  (2021-01-21T16:17:39Z)
• Blind Federated Edge Learning [93.29571175702735]
  We study federated edge learning (FEEL), where wireless edge devices, each with its own dataset, learn a global model. We propose an analog over-the-air' aggregation scheme, in which the devices transmit their local updates in an uncoded fashion.
  arXiv  Detail & Related papers  (2020-10-19T16:22:28Z)
• Antenna Selection for Improving Energy Efficiency in XL-MIMO Systems [31.497083605792685]
  We consider the recently proposed extra-large scale massive multiple-input multiple-output (XL-MIMO) systems. From a green perspective, it is not effective to simultaneously activate hundreds or even thousands of antennas. We propose four antenna selection (AS) approaches to be deployed in XL-MIMO systems aiming at maximizing the total energy efficiency (EE)
  arXiv  Detail & Related papers  (2020-09-05T14:43:21Z)
• Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMO [54.54508321463112]
  In massive multiple-input multiple-output (MIMO) systems, the large number of antennas would bring a great challenge for the acquisition of the accurate channel state information. We utilize the neural networks (NNs) to capture the inherent connection between the uplink and downlink channel data sets and extrapolate the downlink channels from a subset of the uplink channel state information. We study the antenna subset selection problem in order to achieve the best channel extrapolation and decrease the data size of NNs.
  arXiv  Detail & Related papers  (2020-09-03T13:38:52Z)
• Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning [56.94644428312295]
  Wireless connectivity is instrumental in enabling federated learning (FL) Channel randomnessperturbs each worker inversions model update while multiple workers updates incur significant interference on bandwidth. In A-FADMM, all workers upload their model updates to the parameter server using a single channel via analog transmissions. This not only saves communication bandwidth, but also hides each worker's exact model update trajectory from any eavesdropper.
  arXiv  Detail & Related papers  (2020-07-03T16:31:15Z)

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.