Federated Semantic Communication for the Metaverse: Architecture, Evaluation, and Research Directions | Chapter 2 |New Horizons of Science, Technology and Culture Vol. 1

 

The Metaverse is a virtual digital space comprising multiple virtual subspaces that coexist and engage with the real world, establishing a collective virtual reality setting. A distinctive characteristic of the Metaverse is its inherent linkage between the physical and virtual domains; the former represents the physical space of the real world, while the latter comprises the virtual spaces within the Metaverse. This chapter investigates key technological aspects essential to the development of the Metaverse, including high-quality data processing, immersive experience mechanisms, large-scale access, and robust privacy and security. It presents a comprehensive analysis of the Metaverse’s service requirements, emphasising the necessity of low latency, large-scale data storage, robust security, and efficient data transmission to support immersive and reliable virtual environments. To address these requirements, a federated semantic communication framework is proposed, integrating semantic data transmission, semantic twins, and a Metaverse construction model trained via federated learning. The framework’s effectiveness is evaluated through simulations conducted on MNIST, KMNIST, and CIFAR10 datasets, using Peak Signal-to-Noise Ratio (PSNR) and classification accuracy as principal evaluation metrics. Experimental results demonstrate substantial improvements in transmission efficiency, data recovery quality, and intelligent recognition capabilities. Moreover, the framework achieves a low compression rate with minimal information distortion, thereby reducing transmission delays and enhancing the immersive quality of Metaverse environments. Finally, this chapter discusses future challenges and outlines potential research directions for advancing semantic communication, federated learning, and Metaverse technologies.

 

Author (s) Details

Ding Xuhui
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China.

 

Chen Jiawen
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China.

 

Zhang Yuanyuan
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China.

 

Please see the book here:- https://doi.org/10.9734/bpi/nhstc/v1/5453

Federated Learning for Secure Genomic Research: Privacy-Preserving AI Solutions for Precision Medicine | Chapter 3 | Science and Technology: Developments and Applications Vol. 9

The applications of AI technology in genomic research are increasing, and as centralized AI models need to be trained on aggregated raw genomic data, federated learning (FL), which is inherently a privacy-preserving AI approach, has evolved. Importantly, FL enables multiple institutions to jointly train machine learning models without transmitting raw genomic datasets out of the source institution. Nonetheless, it complies with the most intensive data protection laws, such as the GDPR, HIPAA, or PIPL. This paper recognizes FL in promoting the cybersecurity of genomic data and analyzes its potential for counteracting the non-compliance with individual genomic data privacy by first examining a fundamental optimization algorithm for fitting decentralized models, Federated Averaging. It also covers techniques for preserving privacy, such as Homomorphic Encryption and Differential Privacy. For one, HE is about performing encrypted computations on genomic data to mitigate the risk of adversarial inference attacks, while DP injects carefully designed noise into model updates to alleviate risks of data reconstruction and membership inference attacks. In addition, the paper comments on the ethical and regulatory aspects of genomic FL, such as data ownership, cybersecurity hazards of training on sensitive data, and the trustworthiness of the produced models. It also draws attention to critical future research areas: hybrid encryption methods, quantum-safe cryptographic protocols, and cross-compatibility standards to improve the effectiveness of FL over dissimilar genomic data. These developments represent the beginning of the potential for transforming genomic AI research while preserving privacy using FL.

 

Author (s) Details

 

Alex Mathew
Bethany College, USA.

 

Hannah Alex
University of Pittsburgh, USA.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v9/5116

Federated Learning and Deep Learning: A New Frontier for Real-World Applications | Chapter 9 | Science and Technology: Developments and Applications Vol. 3

Both federated learning and deep learning are two of the most discussed, maybe biggest technologies in real-world application development across many industries and full of business significance, so in this chapter we will take a look at these interdisciplinary research aspects and explain their relationship. Federated learning provides a decentralized framework where models can be trained collaboratively without having to disclose sensitive data. The combination of federated learning and deep le data in diverse domains like healthcare, finance, smart devices, autonomous vehicles, and retail but much more useful due to the fact that deep learning can automatically extract effective and complex feature representation with a sufficient amount of data. The chapter highlights the importance of these two paradigms in solving critical data privacy issues and promoting innovations by examining how they work together, their benefits and drawbacks. It also offers recommendations for future research and applications, stressing the need for ethics in AI and the spirit of interdisciplinary collaboration. This convergence, therefore, represents another frontier in artificial intelligence that can provide solutions that are more secure, inclusive, and impactful in a world that is increasingly driven by data.

Recent research has emerged in the form of federated learning of deep neural networks (DL), which showcases its potential for data privacy in a diverse range of applications, including healthcare, finance, smart devices, autonomous vehicles, and retail.

 

Author (s) Details

Sridhar K.
Department of CSE, Vaageswari College of Engineering, Karimnagar, Telangana, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v3/3199