Unleashing the power of edge-cloud generative ai in mobile networks: A survey of aigc services
Artificial Intelligence-Generated Content (AIGC) is an automated method for generating,
manipulating, and modifying valuable and diverse data using AI algorithms creatively. This …
manipulating, and modifying valuable and diverse data using AI algorithms creatively. This …
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 …
A survey of incentive mechanism design for federated learning
Federated learning is promising in enabling large-scale machine learning by massive
clients without exposing their raw data. It can not only enable the clients to preserve the …
clients without exposing their raw data. It can not only enable the clients to preserve the …
Incentive mechanisms for federated learning: From economic and game theoretic perspective
Federated learning (FL) becomes popular and has shown great potentials in training large-
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …
FedSA: A semi-asynchronous federated learning mechanism in heterogeneous edge computing
Federated learning (FL) involves training machine learning models over distributed edge
nodes (ie, workers) while facing three critical challenges, edge heterogeneity, Non-IID data …
nodes (ie, workers) while facing three critical challenges, edge heterogeneity, Non-IID data …
A survey on trustworthy edge intelligence: From security and reliability to transparency and sustainability
Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push
the capabilities of AI to the network edge for real-time, efficient and secure intelligent …
the capabilities of AI to the network edge for real-time, efficient and secure intelligent …
Resource-efficient federated learning with hierarchical aggregation in edge computing
Federated learning (FL) has emerged in edge computing to address limited bandwidth and
privacy concerns of traditional cloud-based centralized training. However, the existing FL …
privacy concerns of traditional cloud-based centralized training. However, the existing FL …
Multiagent federated reinforcement learning for secure incentive mechanism in intelligent cyber–physical systems
Federated learning (FL) is an emerging technology for empowering various applications that
generate large amounts of data in intelligent cyber–physical systems (ICPS). Though FL can …
generate large amounts of data in intelligent cyber–physical systems (ICPS). Though FL can …
PFLF: Privacy-preserving federated learning framework for edge computing
H Zhou, G Yang, H Dai, G Liu - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Federated learning (FL) can protect clients' privacy from leakage in distributed machine
learning. Applying federated learning to edge computing can protect the privacy of edge …
learning. Applying federated learning to edge computing can protect the privacy of edge …
Stackelberg-game-based computation offloading method in cloud–edge computing networks
Offloading computation tasks through cloud–edge collaboration has been a promising way
to improve the Quality of Service (QoS) of applications. Usually, cloud server (CS) and edge …
to improve the Quality of Service (QoS) of applications. Usually, cloud server (CS) and edge …