mlsanityData to 7 Oct 2026

How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning

arXiv 2112.01039 ↗First posted 2 Dec 2021cs.LG cs.AI cs.DCcited by 6

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Abstract (arXiv)

Federated learning (FL) has recently emerged as a transformative paradigm that jointly train a model with distributed data sets in IoT while avoiding the need for central data collection. Due to the limited observation range, such data sets can only reflect local information, which limits the quality of trained models. In practice, the global information and local observations would require a joint consideration for learning to make a reasonable policy. However, in horizontal FL, the central agency only acts as a model aggregator without utilizing its global observation to further improve the model. This could significantly degrade the performance in some missions such as traffic flow prediction in network systems, where the global information may enhance the accuracy. Meanwhile, the global feature may not be directly transmitted to agents for data security. How to utilize the global observation residing in the central agency while protecting its safety thus rises up as an important problem in FL. In this paper, we develop a vertical-horizontal federated learning (VHFL) process, where the global feature is shared with the agents in a procedure similar to that of vertical FL without any extra communication rounds. By considering the delay and packet loss, we will analyze VHFL convergence and validate its performance by experiments. It is shown that the proposed VHFL could enhance the accuracy compared with horizontal FL while still protecting the security of global data.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 17 of its 25 references that are in the map. First posted on arXiv 2 Dec 2021.

Builds on

17 of 25 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2020Adaptive Federated Optimizationcited by 1,036
  4. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  5. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  6. 2019Federated Machine Learning: Concept and Applicationscited by 409
  7. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  8. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  9. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  10. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  11. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 150
  12. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
5 more
  1. 2021Federated Reconstruction: Partially Local Federated Learningcited by 78
  2. 2016Mean Absolute Percentage Error for regression modelscited by 58
  3. 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
  4. 2021Communication-Efficient Federated Learning with Binary Neural Networkscited by 13
  5. 2021Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theorycited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 0; 2025: 0; 2026: 1.

6 papers.

  1. 2023FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learningcited by 7
  2. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
  3. 2022Asynchronous Federated Learning for Edge-assisted Vehicular Networkscited by 5
  4. 2022Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequencycited by 3
  5. 2023FedNC: A Secure and Efficient Federated Learning Method with Network Codingcited by 2
  6. 2026FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inferencecited by 0