mlsanityData to 7 Oct 2026

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

arXiv 2602.02469 ↗First posted 2 Feb 2026cs.IT cs.LG eess.SPcited by 1

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Abstract (arXiv)

We study federated learning (FL) over wireless fading channels where multiple devices simultaneously send their model updates. We propose an efficient 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 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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 28 of its 38 references that are in the map. First posted on arXiv 2 Feb 2026.

Builds on

28 of 38 references.

References · 28

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2018Federated Learning via Over-the-Air Computationcited by 215
  5. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  6. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 103
  7. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 61
  8. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  9. 2020Blind Federated Edge Learningcited by 42
  10. 2019Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networkscited by 39
  11. 2022Joint Privacy Enhancement and Quantization in Federated Learningcited by 34
  12. 2020rTop-k: A Statistical Estimation Approach to Distributed SGDcited by 27
16 more
  1. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 13
  2. 2022Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learningcited by 10
  3. 2023Delay Sensitive Hierarchical Federated Learning with Stochastic Local Updatescited by 8
  4. 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approachcited by 6
  5. 2024Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approachcited by 5
  6. 2024FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantizationcited by 5
  7. 2024NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detectioncited by 4
  8. 2023Value of Information and Timing-aware Scheduling for Federated Learningcited by 3
  9. 2025Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learningcited by 3
  10. 2025Age-Aware Partial Gradient Update Strategy for Federated Learning Over the Aircited by 2
  11. 2023Beamforming and Device Selection Design in Federated Learning with Over-the-air Aggregationcited by 1
  12. 2023CSMAAFL: Client Scheduling and Model Aggregation in Asynchronous Federated Learningcited by 1
  13. 2023Over-the-Air Federated Learning with Compressed Sensing: Is Sparsification Necessary?cited by 1
  14. 2023Federated Learning via Active RIS Assisted Over-the-Air Computationcited by 1
  15. 2024rAge-k: Communication-Efficient Federated Learning Using Age Factorcited by 1
  16. 2025SegOTA: Accelerating Over-the-Air Federated Learning with Segmented Transmissioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Eigenspace-Based Clustering for Personalized System Identificationcited by 0