We study the problem of distributed information bottleneck, in which multiple
encoders separately compress their observations in a manner such that,
collectively, the compressed signals preserve as much information as possible
about another signal. The model generalizes Tishby's centralized information
bottleneck method to the setting of multiple distributed encoders. We establish
single-letter characterizations of the information-rate region of this problem
for both i) a class of discrete memoryless sources and ii) memoryless vector
Gaussian sources. Furthermore, assuming a sum constraint on rate or complexity,
for both models we develop Blahut-Arimoto type iterative algorithms that allow
to compute optimal information-rate trade-offs, by iterating over a set of
self-consistent equations.

研究多个编码器分别以某种方式压缩其观察结果的分布式信息瓶颈问题，建立单字母表征信息率区间的模型，并针对离散无记忆源和无记忆向量高斯源，提出 Blahut-Arimoto 类型的迭代算法，通过迭代一组自洽方程来计算最优的信息率与复杂度之间的权衡。