# Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration

arXiv: https://arxiv.org/abs/2504.00282 · HTML: https://mlsanity.com/paper/2504.00282/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning](https://mlsanity.com/paper/2402.06954/) (2024)
- [Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction](https://mlsanity.com/paper/2310.08670/) (2023)
- [Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion](https://mlsanity.com/paper/2502.03664/) (2025)
- [RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation](https://mlsanity.com/paper/2405.15182/) (2024)
- [Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentation](https://mlsanity.com/paper/2502.03813/) (2025)
- [A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning](https://mlsanity.com/paper/2502.09086/) (2025)
- [AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP](https://mlsanity.com/paper/2501.14745/) (2024)
- [A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models](https://mlsanity.com/paper/2502.03386/) (2025)
- [Multi-Scale Transformer Architecture for Accurate Medical Image Classification](https://mlsanity.com/paper/2502.06243/) (2025)

## Built on by (most cited first, 9 of 9)

- [A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention](https://mlsanity.com/paper/2504.15223/) (2025)
- [Structure-Aware Temporal Modeling for Chronic Disease Progression Prediction](https://mlsanity.com/paper/2508.14942/) (2025)
- [Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks](https://mlsanity.com/paper/2505.08220/) (2025)
- [Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2508.14503/) (2025)
- [Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models](https://mlsanity.com/paper/2508.14427/) (2025)
- [Time-Series Learning for Proactive Fault Prediction in Distributed Systems with Deep Neural Structures](https://mlsanity.com/paper/2505.20705/) (2025)
- [Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning](https://mlsanity.com/paper/2505.00299/) (2025)
- [Federated Anomaly Detection for Multi-Tenant Cloud Platforms with Personalized Modeling](https://mlsanity.com/paper/2508.10255/) (2025)
- [Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networks](https://mlsanity.com/paper/2508.07122/) (2025)

## Abstract (arXiv)

This paper proposes a data privacy protection framework based on federated learning, which aims to realize effective cross-domain data collaboration under the premise of ensuring data privacy through distributed learning. Federated learning greatly reduces the risk of privacy breaches by training the model locally on each client and sharing only model parameters rather than raw data. The experiment verifies the high efficiency and privacy protection ability of federated learning under different data sources through the simulation of medical, financial, and user data. The results show that federated learning can not only maintain high model performance in a multi-domain data environment but also ensure effective protection of data privacy. The research in this paper provides a new technical path for cross-domain data collaboration and promotes the application of large-scale data analysis and machine learning while protecting privacy.