PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection
- URL: http://arxiv.org/abs/2406.02318v2
- Date: Thu, 4 Jul 2024 11:00:25 GMT
- Title: PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection
- Authors: Ronghui Xu, Hao Miao, Senzhang Wang, Philip S. Yu, Jianxin Wang,
- Abstract summary: We propose a.
Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns.
We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
- Score: 51.20479454379662
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: With the proliferation of mobile sensing techniques, huge amounts of time series data are generated and accumulated in various domains, fueling plenty of real-world applications. In this setting, time series anomaly detection is practically important. It endeavors to identify deviant samples from the normal sample distribution in time series. Existing approaches generally assume that all the time series is available at a central location. However, we are witnessing the decentralized collection of time series due to the deployment of various edge devices. To bridge the gap between the decentralized time series data and the centralized anomaly detection algorithms, we propose a Parameter-efficient Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. PeFAD for the first time employs the pre-trained language model (PLM) as the body of the client's local model, which can benefit from its cross-modality knowledge transfer capability. To reduce the communication overhead and local model adaptation cost, we propose a parameter-efficient federated training module such that clients only need to fine-tune small-scale parameters and transmit them to the server for update. PeFAD utilizes a novel anomaly-driven mask selection strategy to mitigate the impact of neglected anomalies during training. A knowledge distillation operation on a synthetic privacy-preserving dataset that is shared by all the clients is also proposed to address the data heterogeneity issue across clients. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
Related papers
- Enhancing Federated Learning with Adaptive Differential Privacy and Priority-Based Aggregation [0.0]
Federated learning (FL) develops global models without direct access to local datasets.
It is possible to access the model updates transferred between clients and servers, potentially revealing sensitive local information to adversaries.
Differential privacy (DP) offers a promising approach to addressing this issue by adding noise to the parameters.
We propose a personalized DP framework that injects noise based on clients' relative impact factors and aggregates parameters.
arXiv Detail & Related papers (2024-06-26T16:55:07Z) - NuwaTS: a Foundation Model Mending Every Incomplete Time Series [24.768755438620666]
We present textbfNuwaTS, a novel framework that repurposes Pre-trained Language Models for general time series imputation.
NuwaTS can be applied to impute missing data across any domain.
We show that NuwaTS generalizes to other time series tasks, such as forecasting.
arXiv Detail & Related papers (2024-05-24T07:59:02Z) - Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays [0.0]
Federated learning (FL) was recently proposed to securely train models with data held over multiple locations ("clients")
Two major challenges hindering the performance of FL algorithms are long training times caused by straggling clients, and a decline in model accuracy under non-iid local data distributions ("client drift")
We propose and analyze Asynchronous Exact Averaging (AREA), a new (sub)gradient algorithm that utilizes communication to speed up convergence and enhance scalability, and employs client memory to correct the client drift caused by variations in client update frequencies.
arXiv Detail & Related papers (2024-05-16T14:22:49Z) - Federated Learning with Projected Trajectory Regularization [65.6266768678291]
Federated learning enables joint training of machine learning models from distributed clients without sharing their local data.
One key challenge in federated learning is to handle non-identically distributed data across the clients.
We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
arXiv Detail & Related papers (2023-12-22T02:12:08Z) - Anomaly Detection through Unsupervised Federated Learning [0.0]
Federated learning is proving to be one of the most promising paradigms for leveraging distributed resources.
We propose a novel method in which, through a preprocessing phase, clients are grouped into communities.
The resulting anomaly detection model is then shared and used to detect anomalies within the clients of the same community.
arXiv Detail & Related papers (2022-09-09T08:45:47Z) - Federated and Generalized Person Re-identification through Domain and
Feature Hallucinating [88.77196261300699]
We study the problem of federated domain generalization (FedDG) for person re-identification (re-ID)
We propose a novel method, called "Domain and Feature Hallucinating (DFH)", to produce diverse features for learning generalized local and global models.
Our method achieves the state-of-the-art performance for FedDG on four large-scale re-ID benchmarks.
arXiv Detail & Related papers (2022-03-05T09:15:13Z) - 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) - Federated Multi-Target Domain Adaptation [99.93375364579484]
Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy.
We consider a more practical scenario where the distributed client data is unlabeled, and a centralized labeled dataset is available on the server.
We propose an effective DualAdapt method to address the new challenges.
arXiv Detail & Related papers (2021-08-17T17:53:05Z) - TadGAN: Time Series Anomaly Detection Using Generative Adversarial
Networks [73.01104041298031]
TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs)
To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics.
To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
arXiv Detail & Related papers (2020-09-16T15:52:04Z) - Anomaly Detection at Scale: The Case for Deep Distributional Time Series
Models [14.621700495712647]
Main novelty in our approach is that instead of modeling time series consisting of real values or vectors of real values, we model time series of probability distributions over real values (or vectors)
Our method is amenable to streaming anomaly detection and scales to monitoring for anomalies on millions of time series.
We show that we outperform popular open-source anomaly detection tools by up to 17% average improvement for a real-world data set.
arXiv Detail & Related papers (2020-07-30T15:48:55Z)
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.