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Wireless Federated k-Means Clustering with Non-coherent Over-the-Air Computation

arXiv 2308.06371 ↗First posted 11 Aug 2023eess.SP cs.AI cs.ITcited by 0

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Abstract (arXiv)

In this study, we propose using an over-the-air computation (OAC) scheme for the federated k-means clustering algorithm to reduce the per-round communication latency when it is implemented over a wireless network. The OAC scheme relies on an encoder exploiting the representation of a number in a balanced number system and computes the sum of the updates for the federated k-means via signal superposition property of wireless multiple-access channels non-coherently to eliminate the need for precise phase and time synchronization. Also, a reinitialization method for ineffectively used centroids is proposed to improve the performance of the proposed method for heterogeneous data distribution. For a customer-location clustering scenario, we demonstrate the performance of the proposed algorithm and compare it with the standard k-means clustering. Our results show that the proposed approach performs similarly to the standard k-means while reducing communication latency.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 11 Aug 2023.

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10 of 19 references.

References · 10

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  3. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  4. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  5. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  6. 2022A Survey on Over-the-Air Computationcited by 21
  7. 20185G New Radio: Unveiling the Essentials of the Next Generation Wireless Access Technologycited by 16
  8. 2022Secure Federated Clusteringcited by 10
  9. 2022Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learningcited by 10
  10. 2022Distributed Learning over a Wireless Network with Non-coherent Majority Vote Computationcited by 9

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  1. 2026Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks
  2. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions
  3. 2024Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air
  4. 2023Unsourced Massive Access-Based Digital Over-the-Air Computation for Efficient Federated Edge Learning
  5. 2024Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach
  6. 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling
  7. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Design
  8. 2025Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-off