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Recent studies have obtained lower bounds on the expected decrease in model loss that is achieved through each round of federated learning. However, convergence generally requires a large number of communication rounds, which induces delay in model training and is costly in terms of network resources. In this paper, we propose a fast-convergent federated learning algorithm, called  $\\mathsf {FOLB}$ , which performs intelligent sampling of devices in each round of model training to optimize the expected convergence speed. We first theoretically characterize a lower bound on improvement that can be obtained in each round if devices are selected according to the expected improvement their local models will provide to the current global model. Then, we show that  $\\mathsf {FOLB}$  obtains this bound through uniform sampling by weighting device updates according to their gradient information.  $\\mathsf {FOLB}$  is able to handle both communication and computation heterogeneity of devices by adapting the aggregations according to estimates of device’s capabilities of contributing to the updates. 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three-state Markov chain model valid for any kind of satellite constellation and for L, S, and Ka bands. Data from measurement campaigns was used to extract the parameters for the statistical distributions involved in the model. We also present a methodology describing how to use the proposed channel model to interface with system-level analysis. We use the time series generated by the model to parameterize power control (PC) imperfection as a function of the environment and user speed by acting upon the time series through a seven-level PC algorithm. We then apply the obtained parameterization to the analysis of a direct sequence-code division multiple-access scenario with users requiring different quality-of-service (QoS). It is shown that request of different QoS produces an unbalance in the users power distribution and less users can be served with the most demanding QoS. PC error worsens this effect and considerably reduces capacity efficiency which is highly dependent on the environment and mobile speed. Bit-error rate statistics are also investigated through the log-normal characterization obtained by using the channel model obtaining results on the effect of required E\u002Fsub b\u002F\u002F(N\u002Fsub o\u002F+I\u002Fsub o\u002F) of a particular user.",{"EN":2828},"LMS Markov model and its use for power control error impact analysis on system capacity",{"EN":2830},"Least squares approximation,Power system modeling,Power control,Error correction,Control system analysis,Error analysis,Statistical distributions,Wideband,Satellite constellations,Data mining",{"VOID":2832},"10.1109\u002F11.664026\n10.1109\u002FT-VT.1985.24048\n10.1109\u002F26.477480\n10.1109\u002F25.845114\npérez fontán perez-fontan, 0, statistical modeling of the lms channel, IEEE Trans Veh Tech\nevaluation rep european agency imt2000 satellite evaluation committee, 0\n10.1109\u002FWCNC.1999.796925\n10.1109\u002F49.286663\n10.1109\u002FVETEC.1996.501436\nÖjanperäojanpera, 1998, Wideband CDMA for Third Generation Mobile Communications\n10.1109\u002F25.669103\nhämäläinen hamalainen, 1997, a novel interface between link and system level simulations, Proc ACTS 97, 599",{"VOID":2834},"10.1109\u002FJSAC.2002.801230","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1021917\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1021917",[2838,2853],{"id":2839,"sortIndex":25,"researcher":24,"roles":2840,"affiliations":2841,"properties":2850,"displayName":2852,"givenName":24,"familyName":24},"db87c553-7508-456d-a089-1c8028fb9cb3",[2512],[2842],{"id":2843,"sortIndex":25,"affiliation":2844,"properties":24},"955b4911-020f-46c5-82de-3275317c0b24",{"id":2843,"createTime":24,"updateTime":24,"relativeEntities":2845,"slug":24,"properties":2846,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":2849,"statistic":24},[],{"title":2847},{"VI":2848},"Departamento de Teoría de la Señal y Comunicaciones, Universidad Carlos III de Madrid, Madrid, Spain",[],{"title":2851},{"VI":2852},"M.A. Vazquez-Castro",{"id":2854,"sortIndex":148,"researcher":24,"roles":2855,"affiliations":2856,"properties":2865,"displayName":2867,"givenName":24,"familyName":24},"d4b6a90d-40b1-4c32-b43f-5b0469a9000a",[2512],[2857],{"id":2858,"sortIndex":25,"affiliation":2859,"properties":24},"b28a02b8-3103-4b57-801c-96aeba10bc90",{"id":2858,"createTime":24,"updateTime":24,"relativeEntities":2860,"slug":24,"properties":2861,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":2864,"statistic":24},[],{"title":2862},{"VI":2863},"Departamento de Tecnologías de las Comunicaciones, Universidad de Vigo, Vigo, Spain",[],{"title":2866},{"VI":2867},"F. 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