We study a notion of guesswork, where multiple agents intend to launch a coordinated brute-force attack to find a single binary secret string, and each agent has access to side information generated through either a BEC or a BSC. The average number of trials required to find the secret string grows exponentially with the length of the string, and the rate of the growth is called the guesswork exponent. We compute the guesswork exponent for several multi-agent attacks. We show that a multi-agent attack reduces the guesswork exponent compared to a single agent, even when the agents do not exchange information to coordinate their attack, and try to individually guess the secret string using a predetermined scheme in a decentralized fashion. Further, we show that the guesswork exponent of two agents who do coordinate their attack is strictly smaller than that of any finite number of agents individually performing decentralized guesswork.
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）
