… FL at the edge. In consideration of resource heterogeneity at the edgenetworks, we then provide … of concepts derived from network economics, optimization, game theory, and machine …
… at the networkedge classifying incoming … a federatedlearning approach that keeps the communication overheads of training small even for scenarios involving many edgenetwork …
… Moreover, the use of FL in mobile edgenetworks was investigated in [… edgenetworks and the roles of FL in edgenetwork optimization. Recently, the potential of FL in wireless networks …
J Ren, W Ni, H Tian - IEEE Communications Letters, 2022 - ieeexplore.ieee.org
… while protecting users privacy, federatedlearning (FL) has grabbed the limelight. However, there are still some challenges when deploying FL in wireless networks. On the one hand, …
… FL framework with a given resource budget in wireless edgenetworks. The authors of [20] … in wireless networks. Recently, coded computing schemes are proposed for FL at the edge [24…
… the opportunities and challenges in federatedlearning. … Incentive design for efficient federated learning in mobile networks… Federatedlearning for edgenetworks: Resource optimization …
Z Zhao, C Feng, W Hong, J Jiang, C Jia… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
… In this paper, we consider the deployment of federatedlearning in wireless networks, which can be implemented via the interactions between a server and multiple clients. In particular, …
… Abstract—Federatedlearning (FL) has emerged in edge computing to address limited … , which divides the edge nodes into K clusters by balanced clustering. The edge nodes in one …
… policy, which allows the networkedges to add/evict contents in … in terms of the cache hit ratio in vehicular edgenetworks. … mobility-aware federatedlearning scheme for edge caching in …