Automated Graph Neural Network Search Under Federated Learning Framework

Wang, Chunnan; Chen, Bozhou; Li, Geng; Wang, Hongzhi

doi:10.1109/tkde.2023.3250264

Cited by 6 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing works pay attention to the GNNs architecture search problem. FL-AGNNS [69] proposes a federated evolutionary optimization strategy to search for suitable GCN architectures. It applies a GCN SuperNet and a weight-sharing strategy to reduce the searching time so that the proposed algorithm can find better GCN models in a short time.…”

Section: A Horizontal Fedgnnsmentioning

confidence: 99%

Federated Graph Neural Networks: Overview, Techniques, and Challenges

Liu,

Xing,

Deng

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

Graph neural networks (GNNs) have attracted extensive research attention in recent years due to their capability to progress with graph data and have been widely used in practical applications. As societies become increasingly concerned with the need for data privacy protection, GNNs face the need to adapt to this new normal. Besides, as clients in federated learning (FL) may have relationships, more powerful tools are required to utilize such implicit information to boost performance. This has led to the rapid development of the emerging research field of federated GNNs (FedGNNs). This promising interdisciplinary field is highly challenging for interested researchers to grasp. The lack of an insightful survey on this topic further exacerbates the entry difficulty. In this article, we bridge this gap by offering a comprehensive survey of this emerging field. We propose a 2-D taxonomy of the FedGNN literature: 1) the main taxonomy provides a clear perspective on the integration of GNNs and FL by analyzing how GNNs enhance FL training as well as how FL assists GNN training and 2) the auxiliary taxonomy provides a view on how FedGNNs deal with heterogeneity across FL clients. Through discussions of key ideas, challenges, and limitations of existing works, we envision future research directions that can help build more robust, explainable, efficient, fair, inductive, and comprehensive FedGNNs.

show abstract

Section: A Horizontal Fedgnnsmentioning

confidence: 99%

Federated Graph Neural Networks: Overview, Techniques, and Challenges

Liu,

Xing,

Deng

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

show abstract

“…The proposed aggregation algorithms are based on the FedAVG , optimal transportation (Y. Lin et al, 2020), SGNN, or Huber loss function. Regarding the tuning of hyperparameters and the network structure, the authors have applied Bayesian optimization (L. Zheng et al, 2021) and evolutionary optimization strategies (C. Wang, Chen, et al, 2021).…”

Section: Fl Applicationsmentioning

confidence: 99%

Towards federated learning: An overview of methods and applications

Silva

Vinagre

Gama

2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Federated learning (FL) is a collaborative, decentralized privacy-preserving method to attach the challenges of storing data and data privacy. Artificial intelligence, machine learning, smart devices, and deep learning have strongly marked the last years. Two challenges arose in data science as a result. First, the regulation protected the data by creating the General Data Protection Regulation, in which organizations are not allowed to keep or transfer data without the owner's authorization. Another challenge is the large volume of data generated in the era of big data, and keeping that data in one only server becomes increasingly tricky. Therefore, the data is allocated into different locations or generated by devices, creating the need to build models or perform calculations without transferring data to a single location. The new term FL emerged as a sub-area of machine learning that aims to solve the challenge of making distributed models with privacy considerations. This survey starts by describing relevant concepts, definitions, and methods, followed by an indepth investigation of federated model evaluation. Finally, we discuss three promising applications for further research: anomaly detection, distributed data streams, and graph representation.

show abstract

“…DP achieves this by adding a controlled amount of noise to the data before it is transmitted to the aggregator. The level of noise is determined by a privacy parameter, which plays a role in balancing the trade-off, as explained in reference [34]. This parameter connects the model to the process of updating from multiple devices, thus enabling the model to improve collectively [37].…”

Section: Background and Related Workmentioning

confidence: 99%

FL-ODP: An Optimized Differential Privacy Enabled Privacy Preserving Federated Learning

Iqbal,

Tariq,

Adnan

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Privacy-preserving methods and techniques aim to safeguard the privacy of individuals and groups while facilitating data sharing for specific purposes. Federated Learning (FL) is a machine learning approach that allows multiple devices or systems to collaboratively train a model without directly sharing their data with each other or a central server. This paper proposes an Optimized Differential Privacy (ODP) approach to ensure the privacy of individual data points while enabling the extraction of valuable information. The proposed model is validated using the MNIST dataset and is analyzed with the FedAvg aggregator. Differential Privacy (DP) with FL is optimized by varying the noise and delta values. The analysis of data privacy is conducted in three phases: the first phase evaluates simple FL, and the second and third phases utilize different DP parameters to achieve optimized results. These optimized results show improved accuracy and privacy, confirming the efficacy of the proposed FL-ODP approach.

show abstract

Automated Graph Neural Network Search Under Federated Learning Framework

Cited by 6 publications

References 24 publications

Federated Graph Neural Networks: Overview, Techniques, and Challenges

Federated Graph Neural Networks: Overview, Techniques, and Challenges

Towards federated learning: An overview of methods and applications

FL-ODP: An Optimized Differential Privacy Enabled Privacy Preserving Federated Learning

Contact Info

Product

Resources

About