Non-IID data re-balancing at IoT edge with peer-to-peer federated learning for anomaly detection

Wang, Han; Muñoz-González, Luis; Eklund, David; Raza, Shahid

doi:10.1145/3448300.3467827

Cited by 39 publications

(11 citation statements)

References 16 publications

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“…A modified version of SMOTE is the kSMOTE method proposed by [14]. kSMOTE takes only k number of samples in the minority class instead of all data samples to reduce computational complexity (Algorithm 1 line 9 − 13).…”

Section: B Fl-m-smote Re-balancing Methodsmentioning

confidence: 99%

“…FedProx [12] adds regularization terms to the loss function to reduce the distance between the global and local models when the clients' data are non-IID, and this makes the averaging of the local models not far from the global optima. Wand et al [14] proposed a decentralized framework for re-balance the local data on each participating client using P2PK-SMOTE. The presented P2PK-KSMOTE method artificially generates synthetic points for the minority class based on random k points.…”

Section: A Federated Learning and Non-iidmentioning

confidence: 99%

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FLY-SMOTE: Re-Balancing the Non-IID IoT Edge Devices Data in Federated Learning System

Younis

Fisichella

2022

IEEE Access

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In recent years, the data available from IoT devices have increased rapidly. Using a machine learning solution to detect faults in these devices requires the release of device data to a central server. However, these data typically contain sensitive information, leading to the need for privacy-preserving distributed machine learning solutions, such as federated learning, where a model is trained locally on the edge device, and only the trained model weights are shared with a central server. Device failure data are typically imbalanced, i.e., the number of failures is minimal compared to the number of normal samples. Therefore, re-balancing techniques are needed to improve the performance of a machine learning model. In this paper, we present FL-M-SMOTE, a new approach to re-balance the data in different non-IID scenarios by generating synthetic data for the minority class in supervised learning tasks using a modified SMOTE method. Our approach takes k samples from the minority class and generates M new synthetic samples based on one of the nearest neighbors of each k sample. An experimental campaign on a real IoT dataset and three well-known public datasets show that the proposed solution improves the balance accuracy without compromising the model's accuracy.

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Section: B Fl-m-smote Re-balancing Methodsmentioning

confidence: 99%

Section: A Federated Learning and Non-iidmentioning

confidence: 99%

FLY-SMOTE: Re-Balancing the Non-IID IoT Edge Devices Data in Federated Learning System

Younis

Fisichella

2022

IEEE Access

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“…The new global model is sent to all clients, which return the F1 scores [f c 1 ] c∈C obtained on their local validation sets with the new global model (line 12). The server computes mean F1 score value f µ 1 , which is used to evaluate the progress of the federated training (lines [13][14][15][16][17][18][19]. If f µ 1 > F 1 , the new global model is saved and the stopping counter sc is set to 0.…”

Section: Methodsmentioning

confidence: 99%

“…Although the proposed solutions show high detection accuracy scores, the use of the vanilla FEDAVG algorithm make them prone to the drawbacks presented in Section II. Finally, an interesting work by Wang et al [16] presents a peer-topeer variation of FL to train a model for anomaly detection in IoT without the need of a central server. To improve convergence and accuracy on non-i.i.d.…”

Section: A Fl In Cybersecuritymentioning

confidence: 99%

FLAD: Adaptive Federated Learning for DDoS Attack Detection

Doriguzzi-Corin¹,

Siracusa²

2022

Preprint

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Federated Learning (FL) has been recently receiving increasing consideration from the cybersecurity community as a way to collaboratively train deep learning models with distributed profiles of cyberthreats, with no disclosure of training data. Nevertheless, the adoption of FL in cybersecurity is still in its infancy, and a range of practical aspects have not been properly addressed yet. Indeed, the Federated Averaging algorithm at the core of the FL concept requires the availability of test data to control the FL process. Although this might be feasible in some domains, test network traffic of newly discovered attacks cannot be always shared without disclosing sensitive information.In this paper, we address the convergence of the FL process in dynamic cybersecurity scenarios, where the trained model must be frequently updated with new recent attack profiles to empower all members of the federation with latest detection features. To this aim, we propose FLAD (adaptive Federated Learning Approach to DDoS attack detection), a FL solution for cybersecurity applications based on an adaptive mechanism that orchestrates the FL process by dynamically assigning more computation to those members whose attacks profiles are harder to learn, without the need of sharing any test data to monitor the performance of the trained model. Using a recent dataset of DDoS attacks, we demonstrate that FLAD outperforms the original FL algorithm in terms of convergence time and accuracy across a range of unbalanced datasets of heterogeneous DDoS attacks. We also show the robustness of our approach in a realistic scenario, where we retrain the deep learning model multiple times to introduce the profiles of new attacks on a pre-trained model.

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“…Jeong et al [33] propose to generate new samples using a globally trained conditional generative adversarial network (CGAN) to build unskewed local datasets. Similarly, Wang et al generate synthetic data in the minority class based on linear interpolation to re-balance local datasets on edge devices [15]. These approaches avoid the leakage of FL clients' private data.…”

Section: Introductionmentioning

confidence: 99%

Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT

Li¹,

He²,

Yu³

et al. 2022

Preprint

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Nowadays, the industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate on decentralized devices in modern factories. To protect the confidentiality of industrial data, federated learning (FL) was introduced to collaboratively train shared machine learning models. However, the local data collected by different devices skew in class distribution and degrade industrial FL performance. This challenge has been widely studied at the mobile edge, but they ignored the rapidly changing streaming data and clustering nature of factory devices, and more seriously, they may threaten data security. In this paper, we propose FEDGS, which is a hierarchical cloud-edge-end FL framework for 5G empowered industries, to improve industrial FL performance on non-i.i.d. data. Taking advantage of naturally clustered factory devices, FEDGS uses a gradient-based binary permutation algorithm (GBP-CS) to select a subset of devices within each factory and build homogeneous super nodes participating in FL training. Then, we propose a compound-step synchronization protocol to coordinate the training process within and among these super nodes, which shows great robustness against data heterogeneity. The proposed methods are time-efficient and can adapt to dynamic environments, without exposing confidential industrial data in risky manipulation. We prove that FEDGS has better convergence performance than FedAvg and give a relaxed condition under which FEDGS is more communication-efficient. Extensive experiments show that FEDGS improves accuracy by 3.5% and reduces training rounds by 59% on average, confirming its superior effectiveness and efficiency on non-i.i.d. data.

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Non-IID data re-balancing at IoT edge with peer-to-peer federated learning for anomaly detection

Abstract: The increase of the computational power in edge devices has enabled the penetration of distributed machine learning technologies such as federated learning, which allows to build collaborative models performing the training locally in the edge devices, im-

Cited by 39 publications

References 16 publications

FLY-SMOTE: Re-Balancing the Non-IID IoT Edge Devices Data in Federated Learning System

FLY-SMOTE: Re-Balancing the Non-IID IoT Edge Devices Data in Federated Learning System

FLAD: Adaptive Federated Learning for DDoS Attack Detection

Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT

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