Federated learning in cloud-edge collaborative architecture: key technologies, applications and challenges

Bao, Gang; Guo, Ping

doi:10.1186/s13677-022-00377-4

Cited by 36 publications

(17 citation statements)

References 124 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Using FL helps keep the data on their local server more confidential and preserves privacy. Additionally, our system involves blockchain technology, which provides an immutable ledger to facilitate faster information reception, more accurate data processing, risk reduction, transaction recording, asset tracking in a business network, and more [16,17].…”

Section: Methodsmentioning

confidence: 99%

“…These updated parameters iteratively proceed forward and backward until reaching the target with the minimum error, as explained in Algorithm 1 [14,15]. Integrating of FL and blockchain in credit card services ensure preserved privacy, data protection, decentralized storage, secure payment networks, and automated tasks [16,17]. [14].…”

Section: Introductionmentioning

confidence: 99%

“…Blockchain is a distributed ledger (database) shared among a computer network's nodes, used to store datasets in blocks linked together via cryptographic hashes. Blockchain is a new technology that features decentralized storage, tamper resistance, and traceability[16,17,[22][23][24].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems

Baabdullah,

Alzahrani,

Rawat

et al. 2024

Future Internet

View full text Add to dashboard Cite

Increasing global credit card usage has elevated it to a preferred payment method for daily transactions, underscoring its significance in global financial cybersecurity. This paper introduces a credit card fraud detection (CCFD) system that integrates federated learning (FL) with blockchain technology. The experiment employs FL to establish a global learning model on the cloud server, which transmits initial parameters to individual local learning models on fog nodes. With three banks (fog nodes) involved, each bank trains its learning model locally, ensuring data privacy, and subsequently sends back updated parameters to the global learning model. Through the integration of FL and blockchain, our system ensures privacy preservation and data protection. We utilize three machine learning and deep neural network learning algorithms, RF, CNN, and LSTM, alongside deep optimization techniques such as ADAM, SGD, and MSGD. The SMOTE oversampling technique is also employed to balance the dataset before model training. Our proposed framework has demonstrated efficiency and effectiveness in enhancing classification performance and prediction accuracy.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems

Baabdullah,

Alzahrani,

Rawat

et al. 2024

Future Internet

View full text Add to dashboard Cite

show abstract

“…The study also demonstrates how defect management practices in multi-cloud IoT installations may be improved in real-world scenarios by utilizing the generated knowledge graph. The authors of the research study Guanming Bao & Ping Guo(2022) [22] investigate the integration of federated learning into cloud-edge collaborative systems. Federated learning is a decentralized machine learning technique that allows models to be trained across multiple decentralized edge devices, hence eliminating the requirement for central data aggregation.…”

Section: Literature Reviewmentioning

confidence: 99%

Recurrent Neural Network (RNN) Inference Cloud Computing

2024

IJFMR

View full text Add to dashboard Cite

The purpose of this technical article is to investigate the convergence of Recurrent Neural Networks (RNNs) with Cloud Computing considering the potential benefits and challenges that may be encountered in bringing these two systems together. Recurrent Neural Networks (RNNs) constitute a particular type of artificial neural network that is capable of processing data sequentially in such a way that it becomes well-suited for applications that involve handling time series or language processing. On the other hand, cloud computing provides computer infrastructures on a scalable basis as well as flexible computation services that are accessible whenever they are needed. This paper targets making RNN applications more efficient and faster with the use of RNN libraries on cloud computing platforms through which the cloud services can be used for things like data processing power and storage. By combining RNNs with Cloud Computing, this paper aims to enhance the efficiency and performance of RNN-based applications by leveraging the cloud's computational power and storage capabilities. The study investigates the impact of deploying RNN inference tasks in the cloud environment, analyzing factors such as latency, cost-effectiveness, and scalability.

show abstract

“…Federated learning is a promising distributed machine learning [10,65,66,70] technique that enables multiple clients to collaboratively train a model without sharing their private data. The technique has garnered significant attention from both the research community and industry, owing to its potential to facilitate privacy-preserving and scalable data analysis.…”

Section: Related Work 41 Distributed Machine Learningmentioning

confidence: 99%

Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service

Khan¹,

Hu²,

Sadoghi³

et al. 2023

Preprint

View full text Add to dashboard Cite

Federated learning (FL) is a distributed machine learning approach that allows multiple clients to learn a shared model collaboratively without sharing their raw data. State-of-the-art FL systems provide an all-in-one solution; for example, users must install and use whichever data management subsystem that is expected by the FL system. This tightly-coupling paradigm is at best a hindrance to the wide adoption of FL solutions; in some domains, such as scientific applications, it could be a deal-breaker by enforcing a specific type of data solution on special hardware and platforms (e.g., high-performance computing clusters without node-local persistent storage). To this end, one natural solution is to decouple the data management functionalities from the FL system, enabling clients to customize their FL applications with specific data subsystems on which efficient queries can be made. The technical challenges of such decoupling methods include new system architectures, expressive and structured data models for FL parameters, and security guarantees among FL participants. As a starting point for this line of research, this paper conducts a thorough evaluation and comparison of mainstream database solutions as decoupled services in the context of FL systems. To make a fair comparison, we develop a framework called Data-Decoupling Federated Learning (DDFL), which can run FL workloads and is agnostic of the underlying database system by exposing a unified interface. To evaluate the effectiveness and feasibility of our approach, we compare DDFL with state-of-the-art FL systems that tightly couple data management and computational counterparts. We carry out extensive experiments on various datasets and data management subsystems (e.g., PostgreSQL, MongoDB, Cassandra, Neo4j) and show that DDFL achieves comparable or better performance in terms of training time, inference accuracy, and database query time, while providing more options for clients to tune their FL applications regarding data-related metrics, such as performance, resilience, and usability. Additionally, we provide a detailed qualitative analysis of DDFL when being integrated with mainstream database systems.

show abstract

Federated learning in cloud-edge collaborative architecture: key technologies, applications and challenges

Cited by 36 publications

References 124 publications

Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems

Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems

Recurrent Neural Network (RNN) Inference Cloud Computing

Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service

Contact Info

Product

Resources

About