Realizing the Heterogeneity: A Self-Organized Federated Learning Framework for IoT

Pang, Junjie; Huang, Yan; Xie, Zhenzhen; Han, Qilong; Cai, Zhipeng

doi:10.1109/jiot.2020.3007662

Cited by 148 publications

(58 citation statements)

References 27 publications

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“…Example source application data include camera images, video streaming, temperature, and other environmental sensed values [10]. The sensed data are then sent via possible networking routing which could be fully used for distributed processing [11], especially together with the application layer [12][13][14]. Physical layer choice matters as the emergency level and importance level differ among transported data which should be optimized carefully [15,16].…”

Section: Background and Related Workmentioning

confidence: 99%

A Robust Data‐Driven Method for Multiseasonality and Heteroscedasticity in Time Series Preprocessing

Sun

Shen

et al. 2021

Wireless Communications and Mobile Computing

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Internet of Things (IoT) is emerging, and 5G enables much more data transport from mobile and wireless sources. The data to be transmitted is too much compared to link capacity. Labelling data and transmit only useful part of the collected data or their features is a promising solution for this challenge. Abnormal data are valuable due to the need to train models and to detect anomalies when being compared to already overflowing normal data. Labelling can be done in data sources or edges to balance the load and computing between sources, edges, and centres. However, unsupervised labelling method is still a challenge preventing to implement the above solutions. Two main problems in unsupervised labelling are long-term dynamic multiseasonality and heteroscedasticity. This paper proposes a data-driven method to handle modelling and heteroscedasticity problems. The method contains the following main steps. First, raw data are preprocessed and grouped. Second, main models are built for each group. Third, models are adapted back to the original measured data to get raw residuals. Fourth, raw residuals go through deheteroscedasticity and become normalized residuals. Finally, normalized residuals are used to conduct anomaly detection. The experimental results with real-world data show that our method successfully increases receiver-operating characteristic (AUC) by about 30%.

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Section: Background and Related Workmentioning

confidence: 99%

A Robust Data‐Driven Method for Multiseasonality and Heteroscedasticity in Time Series Preprocessing

Sun

Shen

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…These systems proactively selfmanage themselves by reacting to the changing environment using advanced algorithms in conjunction with high-level human-defined goals and policies. These self-organization capabilities are important to ensure the robustness and survival of the future dense IoT network and therefore have attracted an intense research interest (Milner et al, 2012;Ding et al, 2013;Qiu et al, 2017;Pang et al, 2020). However, there are many open challenges in this space and some notable research directions for the future include dealing with the heterogenous interoperability of the system, designing of optimal self-organizing protocols and routing strategies for large-scale distributed heterogenous IoT networks, and cross-platform behavior optimization.…”

Section: Self-organizationmentioning

confidence: 99%

Grand Challenges in IoT and Sensor Networks

2020

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“…It only involves the parameters shared among multiple parties in training collaborative machine learning models. Thus, FL can significantly lower the privacy risks in collaborative knowledge exchange [12] . These features, combined with the highquality contribution from local city DT, are essential for establishing a prediction model and accumulating knowledge and insights for an unknown virus, such as COVID-19, in a short period.…”

Section: Introductionmentioning

confidence: 99%

Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution

Pang

Huang

Xie

et al. 2021

Tsinghua Sci. Technol.

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143

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The novel coronavirus, COVID-19, has caused a crisis that affects all segments of the population. As the knowledge and understanding of COVID-19 evolve, an appropriate response plan for this pandemic is considered one of the most effective methods for controlling the spread of the virus. Recent studies indicate that a city Digital Twin (DT) is beneficial for tackling this health crisis, because it can construct a virtual replica to simulate factors, such as climate conditions, response policies, and people's trajectories, to help plan efficient and inclusive decisions.However, a city DTsystem relies on long-term and high-quality data collection to make appropriate decisions, limiting its advantages when facing urgent crises, such as the COVID-19 pandemic. Federated Learning (FL), in which all clients can learn a shared model while retaining all training data locally, emerges as a promising solution for accumulating the insights from multiple data sources efficiently. Furthermore, the enhanced privacy protection settings removing the privacy barriers lie in this collaboration. In this work, we propose a framework that fused city DT with FL to achieve a novel collaborative paradigm that allows multiple city DTs to share the local strategy and status quickly. In particular, an FL central server manages the local updates of multiple collaborators (city DTs), providing a global model that is trained in multiple iterations at different city DT systems until the model gains the correlations between various response plans and infection trends. This approach means a collaborative city DT paradigm fused with FL techniques can obtain knowledge and patterns from multiple DTs and eventually establish a "global view" of city crisis management. Meanwhile, it also helps improve each city's DT by consolidating other DT's data without violating privacy rules. In this paper, we use the COVID-19 pandemic as the use case of the proposed framework. The experimental results on a real dataset with various response plans validate our proposed solution and demonstrate its superior performance.

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Realizing the Heterogeneity: A Self-Organized Federated Learning Framework for IoT

Cited by 148 publications

References 27 publications

A Robust Data‐Driven Method for Multiseasonality and Heteroscedasticity in Time Series Preprocessing

A Robust Data‐Driven Method for Multiseasonality and Heteroscedasticity in Time Series Preprocessing

Grand Challenges in IoT and Sensor Networks

Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution

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