Summary of Federated Learning Based Latent Factorization Of Tensors For Privacy-preserving Qos Prediction, by Shuai Zhong et al.
Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction
by Shuai Zhong, Zengtong Tang, Di Wu
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to big data analysis is proposed, addressing the challenge of extracting patterns from user-perspective quality of service (QoS) data in Web services. Latent factorization of tensors (LFT) is employed to analyze high-dimensional and incomplete (HDI) tensors containing temporal pattern information. However, traditional LFT models require central storage of QoS data, compromising user privacy. To mitigate this issue, a federated learning framework based on latent factorization of tensors (FL-LFT) is designed, enabling isolated users to collaboratively train a global LFT model while preserving privacy. The proposed FL-LFT approach outperforms state-of-the-art federated learning methods in terms of prediction accuracy, as demonstrated by extensive experiments on a real-world QoS dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to analyze big data is being developed. It helps people understand patterns in user-quality service information from the internet. Right now, this type of analysis requires keeping all the data in one place, which can be a problem for users who want their personal information kept private. To solve this issue, researchers created a special kind of learning system that lets multiple users work together to analyze data without sharing their personal information. This new approach is better than other methods at predicting what will happen next. |
Keywords
» Artificial intelligence » Federated learning
