We discuss a federated learned compression problem, where the goal is to
learn a compressor from real-world data which is scattered across clients and
may be statistically heterogeneous, yet share a common underlying
representation. We propose a distributed source model that encompasses both
characteristics, and naturally suggests a compressor architecture that uses
analysis and synthesis transforms shared by clients. Inspired by personalized
federated learning methods, we employ an entropy model that is personalized to
each client. This allows for a global latent space to be learned across
clients, and personalized entropy models that adapt to the clients' latent
distributions. We show empirically that this strategy outperforms solely local
methods, which indicates that learned compression also benefits from a shared
global representation in statistically heterogeneous federated settings.

本文提出了一种分布式源模型，旨在从现实世界的分散在客户端上的、可能存在统计异质性但共享共同基础表示的数据中学习压缩器。作者使用了个性化联邦学习方法，从而允许在客户端之间学习全局潜在空间，并采用适应客户潜在分布的个性化熵模型。经验证明，该策略优于仅使用本地方法，这表明在统计异质性的联合设置中，学习压缩也受益于共享全局表示。