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Found 12 papers, 4 papers with code
Bayesian Federated Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing
In this work, we propose a message passing based Bayesian federated learning (BFL) framework to avoid these drawbacks. Specifically, we formulate the problem of deep neural network (DNN) learning and compression and as a sparse Bayesian inference problem, in which group sparse prior is employed to achieve structured model compression.
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.
Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation
Based on our optimization formulation, we propose an alternating Riemannian optimization algorithm with a precoder that enables efficient OTA aggregation of low-rank local models without sacrificing training performance.
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.
Efficient Sparse Coding using Hierarchical Riemannian Pursuit
The proposed scheme leverages the global and local Riemannian geometry of the two-stage optimization problem and facilitates fast implementation for superb dictionary recovery performance by a finite number of samples without atom-by-atom calculation.
Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method
The proposed scheme includes a novel problem formulation and an efficient online algorithm design with convergence analysis.
Line-of-Sight MIMO for High Capacity Millimeter Wave Backhaul in FDD Systems
In this paper, we propose a holistic solution containing TO compensation, PHN estimation, precoder/decorrelator optimization of the LoS MIMO for wireless backhaul, and the interleaving of each part.
Blind Data Detection in Massive MIMO via $\ell_3$-norm Maximization over the Stiefel Manifold
Specifically, we propose a novel $\ell_3$-norm-based formulation to recover the data without channel estimation.
Complete Dictionary Learning via $\ell_p$-norm Maximization
Dictionary learning is a classic representation learning method that has been widely applied in signal processing and data analytics.
An Efficient Algorithm for Designing Optimal CRCs for Tail-Biting Convolutional Codes
Lou et al. proposed DSO CRC design methodology for a given zero-terminated convolutional code (ZTCC), in which the fundamental design principle is to maximize the minimum distance at which an undetectable error event of ZTCC first occurs.
Information Theory Information Theory
Stochastic Successive Convex Approximation for Non-Convex Constrained Stochastic Optimization
The proposed CSSCA algorithm can also handle stochastic non-convex constraints in optimization problems, and it opens the way to solving more challenging optimization problems that occur in many applications.
Information Theory Information Theory
