# Online Vertical Federated Learning for Cooperative Spectrum Sensing

arXiv: https://arxiv.org/abs/2312.11363 · HTML: https://mlsanity.com/paper/2312.11363/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 21 of 21)

- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [UVeQFed: Universal Vector Quantization for Federated Learning](https://mlsanity.com/paper/2006.03262/) (2020)
- [VAFL: a Method of Vertical Asynchronous Federated Learning](https://mlsanity.com/paper/2007.06081/) (2020)
- [Online Deep Learning: Learning Deep Neural Networks on the Fly](https://mlsanity.com/paper/1711.03705/) (2017)
- [Towards Flexible Device Participation in Federated Learning](https://mlsanity.com/paper/2006.06954/) (2020)
- [Vertical Federated Learning: Challenges, Methodologies and Experiments](https://mlsanity.com/paper/2202.04309/) (2022)
- [Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data](https://mlsanity.com/paper/2206.08330/) (2022)
- [FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training](https://mlsanity.com/paper/2008.10838/) (2020)
- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [Label Leakage and Protection from Forward Embedding in Vertical Federated Learning](https://mlsanity.com/paper/2203.01451/) (2022)
- [A Communication Efficient Collaborative Learning Framework for Distributed Features](https://mlsanity.com/paper/1912.11187/) (2019)
- [Defending against Reconstruction Attack in Vertical Federated Learning](https://mlsanity.com/paper/2107.09898/) (2021)
- [Flexible Vertical Federated Learning with Heterogeneous Parties](https://mlsanity.com/paper/2208.12672/) (2022)
- [Fair and efficient contribution valuation for vertical federated learning](https://mlsanity.com/paper/2201.02658/) (2022)
- [Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation](https://mlsanity.com/paper/2202.07253/) (2022)
- [Tighter Regret Analysis and Optimization of Online Federated Learning](https://mlsanity.com/paper/2205.06491/) (2022)
- [On the Local Cache Update Rules in Streaming Federated Learning](https://mlsanity.com/paper/2303.16340/) (2023)
- [Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond](https://mlsanity.com/paper/2207.09087/) (2022)
- [Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning](https://mlsanity.com/paper/2208.03694/) (2022)

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

- [Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2502.08160/) (2025)
- [Multimodal Online Federated Learning with Modality Missing in Internet of Things](https://mlsanity.com/paper/2505.16138/) (2025)
- [Mitigating Modality Quantity and Quality Imbalance in Multimodal Online Federated Learning](https://mlsanity.com/paper/2508.11159/) (2025)
- [Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT](https://mlsanity.com/paper/2404.00466/) (2024)
- [Event-Driven Online Vertical Federated Learning](https://mlsanity.com/paper/2506.14911/) (2025)
- [Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection](https://mlsanity.com/paper/2605.23984/) (2026)

## Abstract (arXiv)

The increasing demand for wireless communication underscores the need to optimize radio frequency spectrum utilization. An effective strategy for leveraging underutilized licensed frequency bands is cooperative spectrum sensing (CSS), which enable multiple secondary users (SUs) to collaboratively detect the spectrum usage of primary users (PUs) prior to accessing the licensed spectrum. The increasing popularity of machine learning has led to a shift from traditional CSS methods to those based on deep learning. However, deep learning-based CSS methods often rely on centralized learning, posing challenges like communication overhead and data privacy risks. Recent research suggests vertical federated learning (VFL) as a potential solution, with its core concept centered on partitioning the deep neural network into distinct segments, with each segment is trained separately. However, existing VFL-based CSS works do not fully address the practical challenges arising from streaming data and the objective shift. In this work, we introduce online vertical federated learning (OVFL), a robust framework designed to address the challenges of ongoing data stream and shifting learning goals. Our theoretical analysis reveals that OVFL achieves a sublinear regret bound, thereby evidencing its efficiency. Empirical results from our experiments show that OVFL outperforms benchmarks in CSS tasks. We also explore the impact of various parameters on the learning performance.