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

arXiv: https://arxiv.org/abs/2112.01039 · HTML: https://mlsanity.com/paper/2112.01039/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 2 Dec 2021.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [On the Convergence of Local Descent Methods in Federated Learning](https://mlsanity.com/paper/1910.14425/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Federated Reconstruction: Partially Local Federated Learning](https://mlsanity.com/paper/2102.03448/) (2021)
- [Mean Absolute Percentage Error for regression models](https://mlsanity.com/paper/1605.02541/) (2016)
- [Convergence Analysis and System Design for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2105.00872/) (2021)
- [Communication-Efficient Federated Learning with Binary Neural Networks](https://mlsanity.com/paper/2110.02226/) (2021)
- [Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory](https://mlsanity.com/paper/2103.16994/) (2021)

## Built on by (most cited first, 6 of 6)

- [FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learning](https://mlsanity.com/paper/2303.06360/) (2023)
- [Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties](https://mlsanity.com/paper/2303.18178/) (2023)
- [Asynchronous Federated Learning for Edge-assisted Vehicular Networks](https://mlsanity.com/paper/2208.01901/) (2022)
- [Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency](https://mlsanity.com/paper/2206.05406/) (2022)
- [FedNC: A Secure and Efficient Federated Learning Method with Network Coding](https://mlsanity.com/paper/2305.03292/) (2023)
- [FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference](https://mlsanity.com/paper/2605.08760/) (2026)

## 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.