A comprehensive survey on client selection strategies in federated learning
Federated learning (FL) has emerged as a promising paradigm for collaborative model
training while preserving data privacy. Client selection plays a crucial role in determining the …
training while preserving data privacy. Client selection plays a crucial role in determining the …
Advanced Deep Learning Models for 6G: Overview, Opportunities and Challenges
The advent of the sixth generation of mobile communications (6G) ushers in an era of
heightened demand for advanced network intelligence to tackle the challenges of an …
heightened demand for advanced network intelligence to tackle the challenges of an …
A survey of trustworthy federated learning: Issues, solutions, and challenges
Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods
The integration of the Internet of Things (IoT) with machine learning (ML) is revolutionizing
how services and applications impact our daily lives. In traditional ML methods, data are …
how services and applications impact our daily lives. In traditional ML methods, data are …
FairDPFL-SCS: Fair Dynamic Personalized Federated Learning with strategic client selection for improved accuracy and fairness
Personalized federated learning (PFL) addresses the significant challenge of non-
independent and identically distributed (non-IID) data across clients in federated learning …
independent and identically distributed (non-IID) data across clients in federated learning …
An efficient personalized federated learning approach in heterogeneous environments: a reinforcement learning perspective
In order to address the problem of data heterogeneity, in recent years, personalized
federated learning has tailored models to individual user data to enhance model …
federated learning has tailored models to individual user data to enhance model …
Blockchain-inspired collaborative cyber-attacks detection for securing metaverse
The heterogeneous connections in metaverse environments pose vulnerabilities to cyber-
attacks. To prevent and mitigate malicious network activities in a distributed metaverse …
attacks. To prevent and mitigate malicious network activities in a distributed metaverse …
Efficient asynchronous federated learning with sparsification and quantization
While data is distributed in multiple edge devices, federated learning (FL) is attracting more
and more attention to collaboratively train a machine learning model without transferring raw …
and more attention to collaboratively train a machine learning model without transferring raw …
A review on client selection models in federated learning
Federated learning (FL) is a decentralized machine learning (ML) technique that enables
multiple clients to collaboratively train a common ML model without them having to share …
multiple clients to collaboratively train a common ML model without them having to share …
Security of federated learning in 6G era: A review on conceptual techniques and software platforms used for research and analysis
Federated Learning (FL) is an emerging Artificial Intelligence (AI) paradigm enabling
multiple parties to train a model collaboratively without sharing their data. With the upcoming …
multiple parties to train a model collaboratively without sharing their data. With the upcoming …