{"title":"Taking Advantage of the Mistakes: Rethinking Clustered Federated Learning for IoT Anomaly Detection","authors":"Jiamin Fan;Kui Wu;Guoming Tang;Yang Zhou;Shengqiang Huang","doi":"10.1109/TPDS.2024.3379905","DOIUrl":null,"url":null,"abstract":"Clustered federated learning (CFL) is a promising solution to address the non-IID problem in the spatial domain for federated learning (FL). However, existing CFL solutions overlook the non-IID issue in the temporal domain and lack consideration of time efficiency. In this work, we propose a novel approach, called \n<italic>ClusterFLADS</i>\n, which takes advantage of the false predictions of the inappropriate global models, together with knowledge of temperature scaling and catastrophic forgetting to reveal distributional similarities between the training data (of different clusters) and the test data. Additionally, we design an efficient feature extraction scheme by exploiting the role of each layer in a neural network's learning process. By strategically selecting model parameters and using PCA for dimensionality reduction, \n<italic>ClusterFLADS</i>\n effectively improves clustering speed. We evaluate \n<italic>ClusterFLADS</i>\n using real-world IoT trace data in various scenarios. Our results show that \n<italic>ClusterFLADS</i>\n accurately and efficiently clusters clients, achieving a 100% true positive rate and low false positives across various data distributions in both the spatial and temporal domains.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Parallel and Distributed Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10476751/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Clustered federated learning (CFL) is a promising solution to address the non-IID problem in the spatial domain for federated learning (FL). However, existing CFL solutions overlook the non-IID issue in the temporal domain and lack consideration of time efficiency. In this work, we propose a novel approach, called
ClusterFLADS
, which takes advantage of the false predictions of the inappropriate global models, together with knowledge of temperature scaling and catastrophic forgetting to reveal distributional similarities between the training data (of different clusters) and the test data. Additionally, we design an efficient feature extraction scheme by exploiting the role of each layer in a neural network's learning process. By strategically selecting model parameters and using PCA for dimensionality reduction,
ClusterFLADS
effectively improves clustering speed. We evaluate
ClusterFLADS
using real-world IoT trace data in various scenarios. Our results show that
ClusterFLADS
accurately and efficiently clusters clients, achieving a 100% true positive rate and low false positives across various data distributions in both the spatial and temporal domains.
期刊介绍:
IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to:
a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing.
b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems.
c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation.
d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.