… Abstract—Federated Learning is a collaborative machine learning framework to train a …
FEDERATED LEARNING SYSTEMS In this section, we first introduce the general learning prob…

… FL at the edge. In consideration of resource heterogeneity at the edge networks, we then
provide … of concepts derived from network economics, optimization, game theory, and machine …

… at the network edge classifying incoming … a federated learning approach that keeps the
communication overheads of training small even for scenarios involving many edge network …

… Moreover, the use of FL in mobile edge networks was investigated in [… edge networks and
the roles of FL in edge network optimization. Recently, the potential of FL in wireless networks …

… while protecting users privacy, federated learning (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 edge networks. The authors of [20] …
in wireless networks. Recently, coded computing schemes are proposed for FL at the edge [24…

… the opportunities and challenges in federated learning. … Incentive design for efficient federated
learning in mobile networks… Federated learning for edge networks: Resource optimization …

… In this paper, we consider the deployment of federated learning in wireless networks, which
can be implemented via the interactions between a server and multiple clients. In particular, …

… Abstract—Federated learning (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 network edges to add/evict contents in … in terms of the cache hit ratio
in vehicular edge networks. … mobility-aware federated learning scheme for edge caching in …