In this paper, we utilize the pump dataset to evaluate the performance of the combination of several federated learning frameworks and time series anomaly detection models. Experimental results show that the proposed framework achieves a test accuracy of 97.2%, which shows its potential to be ...
Time series anomaly detection plays a critical role in ensuring the security of Cyber-Physical Systems (CPS). However, the growing complexity of data acquired from CPS poses significant challenges to conventional anomaly detection methods. Deep learning-based anomaly detection has garnered significant at...
Federated learning is a multiple device collaboration setup designed to solve machine learning problems under framework for aggregation and knowledge transfer in distributed local data. This distributed model ensures the privacy of data at each local node. Owing to its relevance, there has been extensiv...
This repo details the implementation of time series features extraction in federated learning (TSFE). The implementation is based on two industry solutions: OpenMLDB[1]and FATE[2]. OpenMLDB is an open-source full-stack solution by 4Paradigm to facilitate feature engineering in machine learning. It...
iottime-seriesactivity-recognitioninternet-of-thingsedgesemi-supervised-learningautoencoderpersonalizedhypernetworksfederated-learningmetalearningfederated-learning-frameworkpersonalized-federated-learningsemipfl UpdatedJan 16, 2024 Python youpengl/FedCAP Star5 ...
Submit your code now Tasks Edit Federated Learning Time Series Time Series Analysis Datasets Edit CIFAR-10 Results from the Paper Edit Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers. Methods Edit ...
outside the scope of this work, focused on the construction of a federation process using FCMs and without an initial model, but in the philosophy of federated learning, an extra security layer, such as Differential Privacy, could be added at the time of sharing the parameters of the model...
2b. This method provides a clear way to differentiate between normal and malicious patterns of behavior in federated learning, allowing for the detection and identification of poisoning attacks. As shown in Fig. 2b, although the time-series trajectories of malicious nodes generally align with those ...
On the other hand, one of the most promising developments is the introduction and fine-tuning of hybrid deep-learning models. By amalgamating multiple deep neural networks, these hybrid architectures strive to encapsulate the multifaceted nature of wind power time series data. Such a combinatorial ap...
Section 2 introduces the main concepts of time-series forecasting and federated learning and reviews related work on federated time series forecasting. Section 3 defines the federated traffic prediction problem and presents the design of the federated setting as well as the federated aggregation ...