mlsanityData to 14 Oct 2026

Federated Learning-Distillation Alternation for Resource-Constrained IoT

arXiv 2505.20456 ↗First posted 26 May 2025eess.SP cs.LGcited by 2

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Abstract (arXiv)

Federated learning (FL) faces significant challenges in Internet of Things (IoT) networks due to device limitations in energy and communication resources, especially when considering the large size of FL models. From an energy perspective, the challenge is aggravated if devices rely on energy harvesting (EH), as energy availability can vary significantly over time, influencing the average number of participating users in each iteration. Additionally, the transmission of large model updates is more susceptible to interference from uncorrelated background traffic in shared wireless environments. As an alternative, federated distillation (FD) reduces communication overhead and energy consumption by transmitting local model outputs, which are typically much smaller than the entire model used in FL. However, this comes at the cost of reduced model accuracy. Therefore, in this paper, we propose FL-distillation alternation (FLDA). In FLDA, devices alternate between FD and FL phases, balancing model information with lower communication overhead and energy consumption per iteration. We consider a multichannel slotted-ALOHA EH-IoT network subject to background traffic/interference. In such a scenario, FLDA demonstrates higher model accuracy than both FL and FD, and achieves faster convergence than FL. Moreover, FLDA achieves target accuracies saving up to 98% in energy consumption, while also being less sensitive to interference, both relative to FL.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 26 of its 55 references that are in the map. First posted on arXiv 26 May 2025.

Builds on

26 of 55 references.

References · 26

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14 more
  1. 2023Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teachercited by 25
  2. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  3. 2021Federated Learning over Energy Harvesting Wireless Networkscited by 17
  4. 2022FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networkscited by 12
  5. 2021Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfercited by 10
  6. 2021On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSIcited by 9
  7. 2024Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networkscited by 9
  8. 2019Federated learning with multichannel ALOHAcited by 4
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  10. 2019Massive Wireless Energy Transfer: Enabling Sustainable IoT Towards 6G Eracited by 3
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  12. 2023Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHAcited by 2
  13. 2024Zero-energy Devices for 6G: Technical Enablers at a Glancecited by 2
  14. 2019From 5G to 6G: Has the Time for Modern Random Access Come?cited by 1

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Schedulingcited by 0
  2. 2025Feature-Based Semantics-Aware Scheduling for Energy-Harvesting Federated Learningcited by 0