mlsanityData to 7 Oct 2026

Optimization Design for Federated Learning in Heterogeneous 6G Networks

arXiv 2303.08322 ↗First posted 15 Mar 2023cs.LG cs.AI cs.DCcited by 7

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Abstract (arXiv)

With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still at an early age, it is expected that the evolving 6G network will adopt advanced artificial intelligence (AI) technologies to collect, transmit, and learn this valuable data for innovative applications and intelligent services. However, traditional machine learning (ML) approaches require centralizing the training data in the data center or cloud, raising serious user-privacy concerns. Federated learning, as an emerging distributed AI paradigm with privacy-preserving nature, is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. However, there are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenging heterogeneity issues from three aspects: incentive mechanism design, network resource management, and personalized model optimization. We also present some open problems and promising directions for future research.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 15 Mar 2023.

Builds on

9 of 24 references.

References · 9

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  4. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  5. 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 80
  6. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  7. 2019Artificial Intelligence-Enabled Intelligent 6G Networkscited by 27
  8. 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
  9. 20226G Network AI Architecture for Everyone-Centric Customized Servicescited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 3.

7 papers.

  1. 2026HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Expertscited by 11
  2. 2025Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learningcited by 4
  3. 2026Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneitycited by 4
  4. 2026A Survey on AI for 6G: Challenges and Opportunitiescited by 2
  5. 2024Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Samplingcited by 1
  6. 2023FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learningcited by 0
  7. 2025Federated Learning Based Decentralized Adaptive Intelligent Transmission Protocol for Privacy Preserving 6G Networkscited by 0