We analyze the high-SNR regime of the MxK Network MISO channel in which each transmitter has access to a different channel estimate, possibly with different precision. It has been recently shown that, for some regimes, this setting attains the same Degrees-of-Freedom as the ideal centralized setting with perfect CSI sharing, in which all the transmitters are endowed with the best estimate available at any transmitter. This result is restricted by the limitations of the DoF metric, as it only provides information about the slope of growth of the capacity as a function of the SNR, without any insight about the possible performance at a given SNR. To overcome this limitation, we analyze the affine approximation of the rate on the high-SNR regime for this decentralized Network MISO setting for the antenna configurations in which it achieves the DoF of the centralized setting. We show that, for a regime of antenna configurations, it is possible to asymptotically attain the same achievable rate as in the ideal centralized scenario. Consequently, it is possible to achieve the beamforming gain of the ideal perfect-CSI-sharing setting even if only a subset of transmitters is endowed with precise CSI, which can be exploited in scenarios such as distributed massive MIMO where the number of transmit antennas is much bigger than the number of served users. This outcome is a consequence of the synergistic compromise between CSI precision at the transmitters and consistency between the locally-computed precoders, which is an inherent trade-off of decentralized settings that does not exist in a centralized CSI configuration. We propose a precoding scheme achieving the previous result, which is built on an uneven structure in which some transmitters reduce the precision of their own precoding for the sake of using transmission parameters that can be more easily predicted by the other transmitters.
我们考虑的问题，设计分散的计划编码缓存。在此问题中，存在$K$用户，每个用户都缓存$N$总文件库中的$M$文件。问题是最小化$R$，即满足所有用户需求的广播传输数量。去中心化方案允许独立创建每个缓存，允许用户加入或离开而不依赖。先前的工作表明，为了实现编码增益$g$，即$R \leq K (1-M/N)/g$传输，每个文件必须被划分成在$g$中呈指数的子分组的数目。在这项工作中，我们提出了一个简单的翻译计划，转换成一个随机分散的布局方案，保证目标编码增益$g$的任何恒定速率集中的计划。如果原始恒定速率集中式方案中的文件大小在$K$中是次指数的，则所得方案的文件大小在$g$中是次指数的。当新用户加入时，系统的其余部分保持不变。但是，我们需要额外的$O(\log K)$位通信开销来确定新用户的缓存状态。我们还表明，最坏情况下的速率保证下降，只有一个常数因子，由于用户的到达和离开的动态。
Centralized Versus Decentralized Team Games of Distributed Stochastic Differential Decision Systems with Noiseless Information Structures-Part II: Applications
University of Cyprus（塞浦路斯大学）；University of Ottawa（渥太华大学）
Decentralized Machine Learning with Centralized Performance Guarantees via Gibbs Algorithms
1 Centre Inria d'Université Côte d'Azur, INRIA, Sophia Antipolis, France.；2 Laboratoire GAATI, Universit\' e de la Polyn\' e sie fran c aise, Fa`a`\= a , French Polynesia.；3 School of Electrical；Electronic Engineering, University of Sheffield, Sheffield, United Kingdom.；4 ECE Dept. Princeton University, Princeton, 08544 NJ, USA.
Centralized Versus Decentralized Team Games of Distributed Stochastic Differential Decision Systems with Noiseless Information Structures-Part I: General Theory
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization
CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；Aalborg University（奥尔堡大学）；University of Twente（特温特大学）；Netherlands Defence Academy（荷兰国防学院）；Delft University of Technology（代尔夫特理工大学）
