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Found 4 papers, 2 papers with code
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems
In this paper, we propose an optimization-based quantized FL algorithm, which can appropriately fit a general edge computing system with uniform or nonuniform computing and communication resources at the workers.
An Optimization Framework for Federated Edge Learning
To explore the full potential of FL in such an edge computing system, we first present a general FL algorithm, namely GenQSGD, parameterized by the numbers of global and local iterations, mini-batch size, and step size sequence.
Optimization-Based GenQSGD for Federated Edge Learning
Then, we optimize the algorithm parameters to minimize the energy cost under the time constraint and convergence error constraint.
Sample-based and Feature-based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCA
We show that the proposed FL algorithms converge to stationary points and Karush-Kuhn-Tucker (KKT) points of the respective unconstrained and constrained nonconvex problems, respectively.
