Federated Learning Using Coupled Tensor Train Decomposition: Privacy-Preserving Distributed Machine Learning

Conceitos essenciais

The author proposes a coupled tensor train decomposition approach for privacy-preserving federated learning, achieving data confidentiality and computational efficiency.

Resumo

The content discusses the use of coupled tensor train decomposition for federated learning networks to ensure data privacy protection while maintaining computational efficiency. The proposed method extracts common features across different network nodes, demonstrating superior performance over existing methods in terms of accuracy and communication rounds.

Coupled tensor decomposition is applied in a distributed setting to extract shared and individual features from multi-way data sources. The approach ensures privacy preservation by sharing common features while keeping personal information private. Experimental results on synthetic and real datasets validate the effectiveness of the proposed method in achieving accurate classification performance comparable to centralized counterparts.

Key points include the introduction of a coupled tensor train model, development of a privacy-preserving distributed method tailored for federated learning, instantiation for master-slave and decentralized network structures, comparison with existing methods using synthetic and real datasets, and evaluation of algorithm parameter settings.

Estatísticas

Traditional CTD schemes may suffer from low computational efficiency and heavy communication costs.
Experimental results show that CTT-based federated learning achieves almost the same accuracy as centralized counterparts.
Proposed CTT approach is instantiated for master-slave and decentralized networks.
Communication cost can be significantly reduced by transmitting feature mode cores instead of full tensors.
Computational efficiency improves with increasing number of nodes in the network.

Citações

"The proposed CTT approach reduces computation time and communication rounds significantly without compromising accuracy."
"In a classification task, experimental results show that CTT-based federated learning achieves almost the same accuracy performance as that of the centralized counterpart."

Principais Insights Extraídos De

Federated Learning Using Coupled Tensor Train Decomposition

by Xiangtao Zha... às arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.02898.pdf

Perguntas Mais Profundas

How does the use of coupled tensor train decomposition impact scalability in large-scale federated learning networks

The use of coupled tensor train decomposition can have a significant impact on scalability in large-scale federated learning networks. By extracting common features from coupled data while maintaining individual features, CTT allows for efficient processing of distributed multi-way data. This approach enables the extraction of shared and distinct features across different network nodes, ensuring privacy preservation while achieving robustness and accuracy in model training. Additionally, by decomposing the distributed data into tensor trains with shared factors, CTT reduces computational complexity and communication costs, making it well-suited for handling large-scale datasets in federated learning scenarios.

What are potential drawbacks or limitations of implementing privacy-preserving distributed methods like CTT

While privacy-preserving distributed methods like CTT offer numerous benefits, there are potential drawbacks and limitations to consider. One limitation is the trade-off between privacy protection and computational efficiency. Implementing complex encryption techniques or secure communication protocols to ensure data confidentiality may introduce additional overhead and complexity to the system, impacting performance. Moreover, ensuring compliance with regulatory requirements such as GDPR or HIPAA adds another layer of complexity to implementing privacy-preserving methods effectively. Additionally, there may be challenges related to interoperability with existing systems or compatibility issues when integrating these methods into real-world applications.

How might advancements in tensor decomposition techniques influence future developments in machine learning algorithms

Advancements in tensor decomposition techniques have the potential to significantly influence future developments in machine learning algorithms. Tensor decomposition offers a powerful framework for modeling high-dimensional data structures efficiently by capturing complex relationships among multiple dimensions or modes simultaneously. By leveraging advanced tensor factorization methods like tensor train decomposition (TTD) or coupled tensor train decomposition (CTT), researchers can extract meaningful patterns from multidimensional datasets more effectively than traditional matrix-based approaches.
These advancements enable improved feature extraction capabilities, enhanced model interpretability, and better generalization performance across various domains such as image recognition, natural language processing (NLP), recommender systems, and healthcare analytics.
Furthermore,
the ability of tensor decomposition techniques
to handle higher-order tensors makes them well-suited for analyzing complex data structures commonly encountered in modern machine learning tasks.
As research continues to evolve in this area,
we can expect further innovations that leverage
tensor-based models to address new challenges
and push the boundaries of what is possible
in machine learning algorithms.