mlsanityData to 14 Oct 2026

Value of Information and Timing-aware Scheduling for Federated Learning

arXiv 2312.10512 ↗First posted 16 Dec 2023cs.NI cs.LGcited by 3

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Abstract (arXiv)

Data possesses significant value as it fuels advancements in AI. However, protecting the privacy of the data generated by end-user devices has become crucial. Federated Learning (FL) offers a solution by preserving data privacy during training. FL brings the model directly to User Equipments (UEs) for local training by an access point (AP). The AP periodically aggregates trained parameters from UEs, enhancing the model and sending it back to them. However, due to communication constraints, only a subset of UEs can update parameters during each global aggregation. Consequently, developing innovative scheduling algorithms is vital to enable complete FL implementation and enhance FL convergence. In this paper, we present a scheduling policy combining Age of Update (AoU) concepts and data Shapley metrics. This policy considers the freshness and value of received parameter updates from individual data sources and real-time channel conditions to enhance FL's operational efficiency. The proposed algorithm is simple, and its effectiveness is demonstrated through simulations.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 24 references that are in the map. First posted on arXiv 16 Dec 2023.

Builds on

18 of 24 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  3. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 629
  4. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
  5. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  6. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  8. 2018Federated Learning via Over-the-Air Computationcited by 219
  9. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  10. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 153
  11. 2019Measure Contribution of Participants in Federated Learningcited by 79
  12. 2020Age of Information: An Introduction and Surveycited by 70
6 more
  1. 2019Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networkscited by 39
  2. 2020Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Datasetcited by 34
  3. 2018On the Role of Age of Information in the Internet of Thingscited by 11
  4. 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approachcited by 10
  5. 2020Timely Communication in Federated Learningcited by 9
  6. 2018The Cost of Delay in Status Updates and their Value: Non-linear Ageingcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.

  1. 2024Version age-based client scheduling policy for federated learningcited by 5
  2. 2024Load Balancing in Federated Learningcited by 1
  3. 2026Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregationcited by 1