[HTML][HTML] Model aggregation techniques in federated learning: A comprehensive survey
Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …
models to be trained on client devices while ensuring the privacy of user data. Model …
Heterogeneous federated learning: State-of-the-art and research challenges
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …
scale industrial applications. Existing FL works mainly focus on model homogeneous …
PPFL: Privacy-preserving federated learning with trusted execution environments
We propose and implement a Privacy-preserving Federated Learning (PPFL) framework for
mobile systems to limit privacy leakages in federated learning. Leveraging the widespread …
mobile systems to limit privacy leakages in federated learning. Leveraging the widespread …
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 …
AUCTION: Automated and quality-aware client selection framework for efficient federated learning
The emergency of federated learning (FL) enables distributed data owners to collaboratively
build a global model without sharing their raw data, which creates a new business chance …
build a global model without sharing their raw data, which creates a new business chance …
On demand fog federations for horizontal federated learning in IoV
Federated learning using fog computing can suffer from the dynamic behavior of some of the
participants in its training process, especially in Internet-of-Vehicles where vehicles are the …
participants in its training process, especially in Internet-of-Vehicles where vehicles are the …
Towards fairness-aware federated learning
Recent advances in federated learning (FL) have brought large-scale collaborative machine
learning opportunities for massively distributed clients with performance and data privacy …
learning opportunities for massively distributed clients with performance and data privacy …
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) …
Edge-native intelligence for 6G communications driven by federated learning: A survey of trends and challenges
New technological advancements in wireless networks have enlarged the number of
connected devices. The unprecedented surge of data volume in wireless systems …
connected devices. The unprecedented surge of data volume in wireless systems …
Stochastic client selection for federated learning with volatile clients
Federated learning (FL), arising as a privacy-preserving machine learning paradigm, has
received notable attention from the public. In each round of synchronous FL training, only a …
received notable attention from the public. In each round of synchronous FL training, only a …