The error patterns of a wireless channel can be represented by a binary sequence of ones(burst) and zeros(run),which is referred to as a trace.Recent surveys have shown that the run length distribution of a wireless c...The error patterns of a wireless channel can be represented by a binary sequence of ones(burst) and zeros(run),which is referred to as a trace.Recent surveys have shown that the run length distribution of a wireless channel is an intrinsically heavy-tailed distribution.Analytical models to characterize such features have to deal with the trade-off between complexity and accuracy.In this paper,we use an independent but not identically distributed(inid) stochastic process to characterize such channel behavior and show how to parameterize the inid bit error model on the basis of a trace.The proposed model has merely two parameters both having intuitive meanings and can be easily figured out from a trace.Compared with chaotic maps,the inid bit error model is simple for practical use but can still be deprived from heavy-tailed distribution in theory.Simulation results demonstrate that the inid model can match the trace,but with fewer parameters.We then propose an improvement on the inid model to capture the 'bursty' nature of channel errors,described by burst length distribution.Our theoretical analysis is supported by an experimental evaluation.展开更多
个性化联邦学习侧重于为各客户端提供个性化模型,旨在提高对异构数据的处理性能,然而现有的个性化联邦学习算法大多以增加客户端参数量为代价提高个性化模型的性能,使计算变得复杂.为了解决此问题,文中提出基于稀疏正则双层优化的个性...个性化联邦学习侧重于为各客户端提供个性化模型,旨在提高对异构数据的处理性能,然而现有的个性化联邦学习算法大多以增加客户端参数量为代价提高个性化模型的性能,使计算变得复杂.为了解决此问题,文中提出基于稀疏正则双层优化的个性化联邦学习算法(Personalized Federated Learning Based on Sparsity Regularized Bi-level Optimization,pFedSRB),在客户端的个性化更新中引入l 1范数稀疏正则化,提升个性化模型的稀疏度,避免不必要的客户端参数更新,降低模型复杂度.将个性化联邦学习建模为双层优化问题,内层优化采用交替方向乘子法,可提高学习速度.在4个联邦学习基准数据集上的实验表明,pFedSRB在异构数据上表现出色,在提高模型性能的同时有效降低训练用时和空间成本.展开更多
基金Project supported by the National Natural Science Foundationof China (Nos. 61103010,61103190,and 60803100)the National Basic Research Program (973) of China (No. 2012CB933500)the High-Tech R&D Program (863) of China (No.2012AA011001)
文摘The error patterns of a wireless channel can be represented by a binary sequence of ones(burst) and zeros(run),which is referred to as a trace.Recent surveys have shown that the run length distribution of a wireless channel is an intrinsically heavy-tailed distribution.Analytical models to characterize such features have to deal with the trade-off between complexity and accuracy.In this paper,we use an independent but not identically distributed(inid) stochastic process to characterize such channel behavior and show how to parameterize the inid bit error model on the basis of a trace.The proposed model has merely two parameters both having intuitive meanings and can be easily figured out from a trace.Compared with chaotic maps,the inid bit error model is simple for practical use but can still be deprived from heavy-tailed distribution in theory.Simulation results demonstrate that the inid model can match the trace,but with fewer parameters.We then propose an improvement on the inid model to capture the 'bursty' nature of channel errors,described by burst length distribution.Our theoretical analysis is supported by an experimental evaluation.
文摘个性化联邦学习侧重于为各客户端提供个性化模型,旨在提高对异构数据的处理性能,然而现有的个性化联邦学习算法大多以增加客户端参数量为代价提高个性化模型的性能,使计算变得复杂.为了解决此问题,文中提出基于稀疏正则双层优化的个性化联邦学习算法(Personalized Federated Learning Based on Sparsity Regularized Bi-level Optimization,pFedSRB),在客户端的个性化更新中引入l 1范数稀疏正则化,提升个性化模型的稀疏度,避免不必要的客户端参数更新,降低模型复杂度.将个性化联邦学习建模为双层优化问题,内层优化采用交替方向乘子法,可提高学习速度.在4个联邦学习基准数据集上的实验表明,pFedSRB在异构数据上表现出色,在提高模型性能的同时有效降低训练用时和空间成本.
