mlsanityData to 7 Oct 2026

Federated Distillation based Indoor Localization for IoT Networks

arXiv 2205.11440 ↗First posted 23 May 2022eess.SP cs.LG cs.NIcited by 6

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Abstract (arXiv)

Federated distillation (FD) paradigm has been recently proposed as a promising alternative to federated learning (FL) especially in wireless sensor networks with limited communication resources. However, all state-of-the art FD algorithms are designed for only classification tasks and less attention has been given to regression tasks. In this work, we propose an FD framework that properly operates on regression learning problems. Afterwards, we present a use-case implementation by proposing an indoor localization system that shows a good trade-off communication load vs. accuracy compared to federated learning (FL) based indoor localization. With our proposed framework, we reduce the number of transmitted bits by up to 98%. Moreover, we show that the proposed framework is much more scalable than FL, thus more likely to cope with the expansion of wireless networks.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 27 of its 77 references that are in the map. First posted on arXiv 23 May 2022.

Builds on

27 of 77 references.

References · 27

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15 more
  1. 2019Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Datacited by 39
  2. 2020Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixupcited by 29
  3. 2020Communication-Efficient Federated Distillationcited by 28
  4. 2018Novel Massive MIMO Channel Sounding Data Applied to Deep Learning-based Indoor Positioningcited by 16
  5. 2020Cooperative Learning via Federated Distillation over Fading Channelscited by 16
  6. 2017A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprintingcited by 15
  7. 2020An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillationcited by 14
  8. 2022A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challengescited by 12
  9. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  10. 2020DNN-based Localization from Channel Estimates: Feature Design and Experimental Resultscited by 10
  11. 2020Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Methodcited by 9
  12. 2021Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Networkcited by 6
  13. 2022Federated Learning-Based Localization with Heterogeneous Fingerprint Databasecited by 5
  14. 2020Analysis of Knowledge Transfer in Kernel Regimecited by 3
  15. 2020RSSI Fingerprinting-based Localization Using Machine Learning in LoRa Networkscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 0; 2026: 1.

6 papers.

  1. 2024Federated Distillation: A Surveycited by 14
  2. 2023Federated Learning based Hierarchical 3D Indoor Localizationcited by 6
  3. 2024FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networkscited by 3
  4. 2024MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusioncited by 3
  5. 2024A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environmentscited by 2
  6. 2026ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localizationcited by 0