Federated Learning for Data Security and Privacy Protection

Guo, Xiaohui

doi:10.1109/paap54281.2021.9720450

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Federated learning employs a distributed machine learning approach that facilitates training directly on devices, obviating the need to share sensitive data with a central server. Despite its advantages, concerns about data privacy and security persist in federated learning [58][59][60][61]. Various techniques, including homomorphic encryption, have been advanced to address these challenges.…”

Section: Data Security Encryption In Federated Learning: Relevant Lit...mentioning

confidence: 99%

“…The experiments revealed that Dubhe's performance, in terms of classification accuracy, is on par with the optimal greedy method, with minimal encryption and communication costs. Another mention is the study in [61], which offered an overview of challenges in federated learning and evaluated existing solutions, notably featuring homomorphic encryption.…”

Section: Data Security Encryption In Federated Learning: Relevant Lit...mentioning

confidence: 99%

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FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

Karras,

Giannaros,

Theodorakopoulos

et al. 2023

Electronics

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In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model consolidation. Notably, we delved into an in-depth examination of adversarial activities within federated learning contexts. The Federated Adversarial Attack for Multi-Task Learning (FAAMT) was thoroughly assessed, unmasking its proficiency in showcasing and exploiting vulnerabilities across various federated learning approaches. Moreover, we offer an incisive evaluation of numerous federated learning defence mechanisms, including Romoa and RFA, in the scope of the FAAMT. Utilising well-defined evaluation metrics and analytical processes, our study demonstrated a resilient framework suitable for managing IoT Big Data across widespread deployments, while concurrently presenting a solid contribution to the progression and discussion surrounding defensive methodologies within the federated learning and IoT areas.

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Section: Data Security Encryption In Federated Learning: Relevant Lit...mentioning

confidence: 99%

Section: Data Security Encryption In Federated Learning: Relevant Lit...mentioning

confidence: 99%

FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

Karras,

Giannaros,

Theodorakopoulos

et al. 2023

Electronics

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“…However, the data may leak the privacy of users during the process of transmission. Hence, it is difficult to train machine learning models by transmitting data from all users to the central server (CS), since the privacy and security of data need to be protected [2][3][4]. To mitigate this issue, FL is proposed in [5,6], where the raw data of the participating devices are kept locally; they participate in global machine learning model training in a collaborative manner by uploading local models instead of uploading data directly to the CS.…”

Section: Introductionmentioning

confidence: 99%

Communication-Efficient Zeroth-Order Adaptive Optimization for Federated Learning

Xie,

Gao,

et al. 2024

Mathematics

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Federated learning has become a prevalent distributed training paradigm, in which local devices collaboratively train learning models without exchanging local data. One of the most dominant frameworks of federated learning (FL) is FedAvg, since it is efficient and simple to implement; here, the first-order information is generally utilized to train the parameters of learning models. In practice, however, the gradient information may be unavailable or infeasible in some applications, such as federated black-box optimization problems. To solve the issue, we propose an innovative zeroth-order adaptive federated learning algorithm without using the gradient information, referred to as ZO-AdaFL, which integrates the zeroth-order optimization algorithm into the adaptive gradient method. Moreover, we also rigorously analyze the convergence behavior of ZO-AdaFL in a non-convex setting, i.e., where ZO-AdaFL achieves convergence to a region close to a stationary point at a speed of O(1/T) (T represents the total iteration number). Finally, to verify the performance of ZO-AdaFL, simulation experiments are performed using the MNIST and FMNIST datasets. Our experimental findings demonstrate that ZO-AdaFL outperforms other state-of-the-art zeroth-order FL approaches in terms of both effectiveness and efficiency.

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Generalized Early-Stage Depression Detection Using Federated Clustering on Mobile Usage

Dwivedi,

Semalty,

Moondra

2024

2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Federated Learning for Data Security and Privacy Protection

Cited by 5 publications

References 15 publications

FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

Communication-Efficient Zeroth-Order Adaptive Optimization for Federated Learning

Generalized Early-Stage Depression Detection Using Federated Clustering on Mobile Usage

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