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<p>With the proliferation of multi-source data and heightened privacy protection demands, securing data while preserving privacy has emerged as a critical challenge for organizations. Current multi-source data analysis approaches frequently fail to adequately capture temporal dependencies and inter-data correlations, limiting their ability to identify patterns and forecast trends effectively. To address these challenges, this paper proposes the Federated Trusted Network (FTN), which combines a Variable Forgetting LSTM module (VF-LSTM) and a Heterogeneous Aggregation GCN module (HA-GCN). The VF-LSTM module introduces a dynamic forgetting mechanism to flexibly capture temporal dependencies across different data types, while the HA-GCN module mines inherent correlations between different types of data through meta-path-based feature aggregation. Furthermore, FTN ensures data privacy and security by deploying trusted computing technology on local models across different application scenarios. Secure aggregation techniques are employed to integrate local model updates and generate a global model on the central server, enabling the identification of cross-domain patterns and insights. Across three healthcare scenarios, FTN demonstrates superior performance with ACC improvements of 1.1-1.3% and F1 improvements of 1.7-2.3% compared to state-of-the-art models. In the hospital healthcare scenario, FTN achieves 93.7% ACC and 93.1% F1 while maintaining privacy protection. These results validate FTN’s effectiveness in addressing temporal dependency and data correlation challenges.</p>

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Dynamic memory-enhanced federated learning framework with trusted computing for multi-source data analysis

  • 期刊论文

  • 发表日期：

    2025年3月11日

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  • Bian Zhu,
  • Ling Niu

摘要

With the proliferation of multi-source data and heightened privacy protection demands, securing data while preserving privacy has emerged as a critical challenge for organizations. Current multi-source data analysis approaches frequently fail to adequately capture temporal dependencies and inter-data correlations, limiting their ability to identify patterns and forecast trends effectively. To address these challenges, this paper proposes the Federated Trusted Network (FTN), which combines a Variable Forgetting LSTM module (VF-LSTM) and a Heterogeneous Aggregation GCN module (HA-GCN). The VF-LSTM module introduces a dynamic forgetting mechanism to flexibly capture temporal dependencies across different data types, while the HA-GCN module mines inherent correlations between different types of data through meta-path-based feature aggregation. Furthermore, FTN ensures data privacy and security by deploying trusted computing technology on local models across different application scenarios. Secure aggregation techniques are employed to integrate local model updates and generate a global model on the central server, enabling the identification of cross-domain patterns and insights. Across three healthcare scenarios, FTN demonstrates superior performance with ACC improvements of 1.1-1.3% and F1 improvements of 1.7-2.3% compared to state-of-the-art models. In the hospital healthcare scenario, FTN achieves 93.7% ACC and 93.1% F1 while maintaining privacy protection. These results validate FTN’s effectiveness in addressing temporal dependency and data correlation challenges.