Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction

• URL: http://arxiv.org/abs/2407.19828v1
• Date: Mon, 29 Jul 2024 09:30:00 GMT
• Title: Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction
• Authors: Shuai Zhong, Zengtong Tang, Di Wu,
• Abstract summary: This article creatively designs a federated learning based on latent factorization of tensors (FL-LFT) It builds a data-oriented federated learning model to enable isolated users to collaboratively train a global LFT model while protecting user's privacy. Experiments on a dataset collected from the real world verify that FL-LFT shows a remarkable increase in prediction accuracy when compared to state-of-the-art federated learning approaches.
• Score: 3.3295360710329738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In applications related to big data and service computing, dynamic connections tend to be encountered, especially the dynamic data of user-perspective quality of service (QoS) in Web services. They are transformed into high-dimensional and incomplete (HDI) tensors which include abundant temporal pattern information. Latent factorization of tensors (LFT) is an extremely efficient and typical approach for extracting such patterns from an HDI tensor. However, current LFT models require the QoS data to be maintained in a central place (e.g., a central server), which is impossible for increasingly privacy-sensitive users. To address this problem, this article creatively designs a federated learning based on latent factorization of tensors (FL-LFT). It builds a data-density -oriented federated learning model to enable isolated users to collaboratively train a global LFT model while protecting user's privacy. Extensive experiments on a QoS dataset collected from the real world verify that FL-LFT shows a remarkable increase in prediction accuracy when compared to state-of-the-art federated learning (FL) approaches.

Related papers

• Dynamic QoS Prediction via a Non-Negative Tensor Snowflake Factorization [14.603788849701768]
  We propose a Non-negative Snowflake Factorization of tensors model to predict unobserved data. A single latent factor-based, nonnegative update on tensor (SLF-NMUT) yields improved predictions for missing data.
  arXiv  Detail & Related papers  (2025-04-24T03:03:22Z)
• Multi-Head Self-Attending Neural Tucker Factorization [5.734615417239977]
  We introduce a neural network-based tensor factorization approach tailored for learning representations of high-dimensional and incomplete (HDI) tensors. The proposed MSNTucF model demonstrates superior performance compared to state-of-the-art benchmark models in estimating missing observations.
  arXiv  Detail & Related papers  (2025-01-16T13:04:15Z)
• SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe [30.03925858123481]
  We propose SFTMix, a novel recipe that elevates instruction-tuning performance beyond the conventional NTP paradigm. Based on training dynamics, we argue that examples with different confidence levels should play distinct roles during the instruction-tuning process. This approach enables SFTMix to significantly outperform NTP across a wide range of instruction-following and healthcare domain-specific SFT tasks.
  arXiv  Detail & Related papers  (2024-10-07T17:52:21Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN [1.5749416770494706]
  Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices. We propose FLIGAN, a novel approach to address the issue of data incompleteness in FL. Our methodology adheres to FL's privacy requirements by generating synthetic data in a federated manner without sharing the actual data in the process.
  arXiv  Detail & Related papers  (2024-03-25T16:49:38Z)
• Federated Knowledge Graph Unlearning via Diffusion Model [5.373752180709173]
  Federated learning (FL) promotes the development and application of artificial intelligence technologies. In this paper, we propose FedDM, a novel framework tailored for machine unlearning in federated knowledge graphs.
  arXiv  Detail & Related papers  (2024-03-13T14:06:51Z)
• Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics [20.781607752797445]
  We propose a two-stage Decoupled Federated learning framework using Feature Statistics (DFL-FS) In the first stage, the server estimates the client's class coverage distributions through masked local feature statistics clustering. In the second stage, DFL-FS employs federated feature regeneration based on global feature statistics to enhance the model's adaptability to long-tailed data distributions.
  arXiv  Detail & Related papers  (2024-03-13T09:24:59Z)
• Privacy-preserving design of graph neural networks with applications to vertical federated learning [56.74455367682945]
  We present an end-to-end graph representation learning framework called VESPER. VESPER is capable of training high-performance GNN models over both sparse and dense graphs under reasonable privacy budgets.
  arXiv  Detail & Related papers  (2023-10-31T15:34:59Z)
• Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks [72.59891661768177]
  Federated learning (FL) enables distributed learning across edge devices while protecting data privacy. We consider a FL framework with partial model pruning and personalization to overcome these challenges. This framework splits the learning model into a global part with model pruning shared with all devices to learn data representations and a personalized part to be fine-tuned for a specific device.
  arXiv  Detail & Related papers  (2023-09-04T21:10:45Z)
• A Momentum-Incorporated Non-Negative Latent Factorization of Tensors Model for Dynamic Network Representation [0.0]
  A large-scale dynamic network (LDN) is a source of data in many big data-related applications. A Latent factorization of tensors (LFT) model efficiently extracts this time pattern. LFT models based on gradient descent (SGD) solvers are often limited by training schemes and have poor tail convergence. This paper proposes a novel nonlinear LFT model (MNNL) based on momentum-ind SGD to make training unconstrained and compatible with general training schemes.
  arXiv  Detail & Related papers  (2023-05-04T12:30:53Z)
• Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning [86.59588262014456]
  Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. We propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG) Our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
  arXiv  Detail & Related papers  (2022-03-17T11:18:17Z)
• Acceleration of Federated Learning with Alleviated Forgetting in Local Training [61.231021417674235]
  Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy. We propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage. Our experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep.
  arXiv  Detail & Related papers  (2022-03-05T02:31:32Z)
• UVeQFed: Universal Vector Quantization for Federated Learning [179.06583469293386]
  Federated learning (FL) is an emerging approach to train such learning models without requiring the users to share their possibly private labeled data. In FL, each user trains its copy of the learning model locally. The server then collects the individual updates and aggregates them into a global model. We show that combining universal vector quantization methods with FL yields a decentralized training system in which the compression of the trained models induces only a minimum distortion.
  arXiv  Detail & Related papers  (2020-06-05T07:10:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.