mlsanityData to 14 Oct 2026

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

arXiv 2504.00282 ↗First posted 31 Mar 2025cs.LG cs.CRcited by 9

Not on the map. Why?

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.

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

9 of 21 references.

References · 9

  1. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 128
  2. 2023Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reductioncited by 24
  3. 2025Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusioncited by 11
  4. 2024RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregationcited by 10
  5. 2025Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentationcited by 9
  6. 2025A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learningcited by 8
  7. 2024AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAPcited by 7
  8. 2025A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Modelscited by 7
  9. 2025Multi-Scale Transformer Architecture for Accurate Medical Image Classificationcited by 5

Built on by

Cited by year

Citing papers per year: 2025: 9.

9 papers.

  1. 2025A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attentioncited by 7
  2. 2025Structure-Aware Temporal Modeling for Chronic Disease Progression Predictioncited by 7
  3. 2025Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networkscited by 6
  4. 2025Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Servicescited by 6
  5. 2025Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Modelscited by 5
  6. 2025Time-Series Learning for Proactive Fault Prediction in Distributed Systems with Deep Neural Structurescited by 4
  7. 2025Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learningcited by 2
  8. 2025Federated Anomaly Detection for Multi-Tenant Cloud Platforms with Personalized Modelingcited by 1
  9. 2025Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networkscited by 0