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Client selection in federated learning: Principles, challenges, and opportunities
As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …
learning (FL) has received tremendous attention from both industry and academia. In a …
Combining federated learning and edge computing toward ubiquitous intelligence in 6G network: Challenges, recent advances, and future directions
Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …
Fairness and privacy preserving in federated learning: A survey
Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …
addresses privacy concerns by allowing participants to collaboratively train machine …
Efficient federated learning for metaverse via dynamic user selection, gradient quantization and resource allocation
Metaverse is envisioned to merge the actual world with a virtual world to bring users
unprecedented immersive feelings. To ensure user experience, federated learning (FL) has …
unprecedented immersive feelings. To ensure user experience, federated learning (FL) has …
Fedas: Bridging inconsistency in personalized federated learning
Abstract Personalized Federated Learning (PFL) is primarily designed to provide
customized models for each client to better fit the non-iid distributed client data which is a …
customized models for each client to better fit the non-iid distributed client data which is a …
A comprehensive survey of federated transfer learning: challenges, methods and applications
Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …
participants to collaboratively train a centralized model with privacy preservation by …
A comprehensive survey on privacy-preserving techniques in federated recommendation systems
Big data is a rapidly growing field, and new developments are constantly emerging to
address various challenges. One such development is the use of federated learning for …
address various challenges. One such development is the use of federated learning for …
How to prevent the poor performance clients for personalized federated learning?
Personalized federated learning (pFL) collaboratively trains personalized models, which
provides a customized model solution for individual clients in the presence of …
provides a customized model solution for individual clients in the presence of …
A systematic literature review on client selection in federated learning
With the arising concerns of privacy within machine learning, federated learning (FL) was
invented in 2017, in which the clients, such as mobile devices, train a model and send the …
invented in 2017, in which the clients, such as mobile devices, train a model and send the …
Fedlga: Toward system-heterogeneity of federated learning via local gradient approximation
Federated learning (FL) is a decentralized machine learning architecture, which leverages a
large number of remote devices to learn a joint model with distributed training data …
large number of remote devices to learn a joint model with distributed training data …