… Abstract—Federated learning can achieve distributed … study an edge-based communication
optimization framework to … location and deploy mobile edge nodes in different network …

… Abstract—In this paper, we study a relay-assisted federated edge learning (FEEL) network
under latency and bandwidth constraints. In this network, N users collaboratively train a …

… , federated edge learning (FEEL) presents a new paradigm, which avoids direct data
transmission by collaboratively training a global learning model across multiple distributed edge …

… Recent years have witnessed the growing interest in federated learning (FL) in edge
computing systems, also referred to as federated edge learning, where the server periodically …

… Based on the analysis, in Section IV we will propose to minimize the optimality gap via
optimizing the power control subject to a set of individual maximum and average power …

… machine learning model with federated edge learning (FEEL… of edge devices and the limited
wireless resources in edge … edge devices send quantized gradients to the edge server via …

… servers learn a shared model using federated learning (FL) … the computational capacity of
edge servers are entangled … , called federated edge intelligence (FEI), that allows edge servers …

… The convergence analysis and optimization results in this paper can be readily extended
to other step size rules, such as the exponential and diminishing step size rules. …

… paper, optimization algorithms for FL based on edge … -edge-cloud’, the local updates of
model parameters are performed on ‘client-edge’, and the global aggregation is between ‘edge-…

… federated edge learning: We propose a communication-efficient (in terms of convergence
speed) L-BFGS optimization … stochastic batches for the federated edge learning framework, as …