mlsanityData to 7 Oct 2026

Digital Ethics in Federated Learning

arXiv 2310.03178 ↗First posted 4 Oct 2023cs.LG cs.CY cs.DCcited by 2

Not on the map. Why?

Abstract (arXiv)

The Internet of Things (IoT) consistently generates vast amounts of data, sparking increasing concern over the protection of data privacy and the limitation of data misuse. Federated learning (FL) facilitates collaborative capabilities among multiple parties by sharing machine learning (ML) model parameters instead of raw user data, and it has recently gained significant attention for its potential in privacy preservation and learning efficiency enhancement. In this paper, we highlight the digital ethics concerns that arise when human-centric devices serve as clients in FL. More specifically, challenges of game dynamics, fairness, incentive, and continuity arise in FL due to differences in perspectives and objectives between clients and the server. We analyze these challenges and their solutions from the perspectives of both the client and the server, and through the viewpoints of centralized and decentralized FL. Finally, we explore the opportunities in FL for human-centric IoT as directions for future development.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 4 Oct 2023.

Builds on

11 of 17 references.

References · 11

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  3. 2021Towards Personalized Federated Learningcited by 462
  4. 2019Fair Resource Allocation in Federated Learningcited by 389
  5. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 185
  6. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  7. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  8. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 39
  9. 2023Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognitioncited by 19
  10. 2023A Survey of Federated Learning for Connected and Automated Vehiclescited by 17
  11. 2023Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Applicationcited by 11

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selectioncited by 10
  2. 2025A Thorough Assessment of the Non-IID Data Impact in Federated Learningcited by 5