# LF2L: Loss Fusion Horizontal Federated Learning Across Heterogeneous Feature Spaces Using External Datasets Effectively: A Case Study in Second Primary Cancer Prediction

arXiv: https://arxiv.org/abs/2603.07249 · HTML: https://mlsanity.com/paper/2603.07249/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 7 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Integrated multimodal artificial intelligence framework for healthcare applications](https://mlsanity.com/paper/2202.12998/) (2022)
- [A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications](https://mlsanity.com/paper/2408.02686/) (2024)
- [Learning sparse features can lead to overfitting in neural networks](https://mlsanity.com/paper/2206.12314/) (2022)
- [Gini Coefficient as a Unified Metric for Evaluating Many-versus-Many Similarity in Vector Spaces](https://mlsanity.com/paper/2411.07983/) (2024)

## Abstract (arXiv)

Second primary cancer (SPC), a new cancer in patients different from previously diagnosed, is a growing concern due to improved cancer survival rates. Early prediction of SPC is essential to enable timely clinical interventions. This study focuses on lung cancer survivors treated in Taiwanese hospitals, where the limited size and geographic scope of local datasets restrict the effectiveness and generalizability of traditional machine learning approaches. To address this, we incorporate external data from the publicly available US-based Surveillance, Epidemiology, and End Results (SEER) program, significantly increasing data diversity and scale. However, the integration of multi-source datasets presents challenges such as feature inconsistency and privacy constraints. Rather than naively merging data, we proposed a loss fusion horizontal federated learning (LF2L) framework that can enable effective cross-institutional collaboration while preserving institutional privacy by avoiding data sharing. Using both common and unique features and balancing their contributions through a shared loss mechanism, our method demonstrates substantial improvements in the prediction performance of SPC. Experiment results show statistically significant improvements in AUROC and AUPRC when compared to localized, horizontal federated, and centralized learning baselines. This highlights the importance of not only acquiring external data but also leveraging it effectively to enhance model performance in real-world clinical model development.