Differentially Private Federated Learning with Heterogeneous Group Privacy

Jiang, Mingna; Wei, Linna; Cai, Guoyue; Wu, Xuangou

doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics60724.2023.00047

2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing &Amp;amp; Communications (GreenCom 2023

DOI: 10.1109/ithings-greencom-cpscom-smartdata-cybermatics60724.2023.00047

|View full text |Cite

Differentially Private Federated Learning with Heterogeneous Group Privacy

Mingna Jiang,

Linna Wei,

Guoyue Cai

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Systematic Literature Review on the Use of Federated Learning and Bioinspired Computing

Marin Machado de Souza,

Holm,

Biczyk

et al. 2024

Electronics

View full text Add to dashboard Cite

Federated learning (FL) and bioinspired computing (BIC), two distinct, yet complementary fields, have gained significant attention in the machine learning community due to their unique characteristics. FL enables decentralized machine learning by allowing models to be trained on data residing across multiple devices or servers without exchanging raw data, thus enhancing privacy and reducing communication overhead. Conversely, BIC draws inspiration from nature to develop robust and adaptive computational solutions for complex problems. This paper explores the state of the art in the integration of FL and BIC, introducing BIC techniques and discussing the motivations for their integration with FL. The convergence of these fields can lead to improved model accuracy, enhanced privacy, energy efficiency, and reduced communication overhead. This synergy addresses inherent challenges in FL, such as data heterogeneity and limited computational resources, and opens up new avenues for developing more efficient and autonomous learning systems. The integration of FL and BIC holds promise for various application domains, including healthcare, finance, and smart cities, where privacy-preserving and efficient computation is paramount. This survey provides a systematic review of the current research landscape, identifies key challenges and opportunities, and suggests future directions for the successful integration of FL and BIC.

show abstract

A Systematic Literature Review on the Use of Federated Learning and Bioinspired Computing

Marin Machado de Souza,

Holm,

Biczyk

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Differentially Private Federated Learning with Heterogeneous Group Privacy

Cited by 1 publication

References 28 publications

A Systematic Literature Review on the Use of Federated Learning and Bioinspired Computing

A Systematic Literature Review on the Use of Federated Learning and Bioinspired Computing

Contact Info

Product

Resources

About