# Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization

arXiv: https://arxiv.org/abs/2305.06279 · HTML: https://mlsanity.com/paper/2305.06279/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 54 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 28 of 28)

- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018)
- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Communication-Efficient Edge AI: Algorithms and Systems](https://mlsanity.com/paper/2002.09668/) (2020)
- [Vertical Federated Learning: Challenges, Methodologies and Experiments](https://mlsanity.com/paper/2202.04309/) (2022)
- [Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations](https://mlsanity.com/paper/2103.10481/) (2021)
- [A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression](https://mlsanity.com/paper/1912.00513/) (2019)
- [Federated Learning via Intelligent Reflecting Surface](https://mlsanity.com/paper/2011.05051/) (2020)
- [Robust Federated Learning with Noisy Communication](https://mlsanity.com/paper/1911.00251/) (2019)
- [Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach](https://mlsanity.com/paper/2011.10282/) (2020)
- [Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management](https://mlsanity.com/paper/2011.14197/) (2020)
- [Cell-Free Massive MIMO for Wireless Federated Learning](https://mlsanity.com/paper/1909.12567/) (2019)
- [Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2004.05843/) (2020)
- [Fast Convergence Algorithm for Analog Federated Learning](https://mlsanity.com/paper/2011.06658/) (2020)
- [Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks](https://mlsanity.com/paper/2206.02398/) (2022)
- [Toward Intelligent Vehicular Networks: A Machine Learning Framework](https://mlsanity.com/paper/1804.00338/) (2018)
- [Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication](https://mlsanity.com/paper/2201.08512/) (2022)
- [Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2103.02270/) (2021)
- [Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning](https://mlsanity.com/paper/2203.14753/) (2022)
- [Relay-Assisted Cooperative Federated Learning](https://mlsanity.com/paper/2107.09518/) (2021)
- [Time Synchronization in 5G Wireless Edge: Requirements and Solutions for Critical-MTC](https://mlsanity.com/paper/1906.06380/) (2019)
- [Collaborative Cloud and Edge Mobile Computing in C-RAN Systems with Minimal End-to-End Latency](https://mlsanity.com/paper/2103.16188/) (2021)
- [Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2109.02353/) (2021)

## Built on by (most cited first, 7 of 7)

- [Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm](https://mlsanity.com/paper/2404.00836/) (2024)
- [Satellite Federated Edge Learning: Architecture Design and Convergence Analysis](https://mlsanity.com/paper/2404.01875/) (2024)
- [Collaborative Edge AI Inference over Cloud-RAN](https://mlsanity.com/paper/2404.06007/) (2024)
- [Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks](https://mlsanity.com/paper/2308.08792/) (2023)
- [Task-Oriented Edge Networks: Decentralized Learning Over Wireless Fronthaul](https://mlsanity.com/paper/2312.01288/) (2023)
- [A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT](https://mlsanity.com/paper/2410.01644/) (2024)
- [BVFLMSP : Bayesian Vertical Federated Learning for Multimodal Survival with Privacy](https://mlsanity.com/paper/2604.02248/) (2026)

## Abstract (arXiv)

Vertical federated learning (FL) is a collaborative machine learning framework that enables devices to learn a global model from the feature-partition datasets without sharing local raw data. However, as the number of the local intermediate outputs is proportional to the training samples, it is critical to develop communication-efficient techniques for wireless vertical FL to support high-dimensional model aggregation with full device participation. In this paper, we propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation by leveraging over-the-air computation (AirComp) and alleviating communication straggler issue with cooperative model aggregation among geographically distributed edge servers. However, the model aggregation error caused by AirComp and quantization errors caused by the limited fronthaul capacity degrade the learning performance for vertical FL. To address these issues, we characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. To improve the learning performance, we establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed. We conduct extensive simulations to demonstrate the effectiveness of the proposed system architecture and optimization framework for vertical FL.