Federated Learning is rapidly evolving beyond the exchange of traditional model weights and gradients, yet existing definitions fail to capture the full scope of modern payloads like synthetic data and federated analytics. This paper addresses the gap by proposing a formal mathematical definition of a federated message that accounts for both utility and privacy. We introduce a taxonomy that organizes these exchanges into three categories: model structures, statistical summaries, and data-conditioned representations. By evaluating these groups based on computational demands, communication costs, and privacy risks, we provide a clearer understanding of the trade-offs involved in decentralized training. Our review of 202 recent publications highlights a significant shift since 2021 toward diverse messaging paradigms, signaling a move away from standard deep learning updates toward more specialized information sharing. This framework provides a structured path for future research to optimize federated systems for varying hardware and security requirements.

翻译：联邦学习正迅速发展，超越了传统模型权重与梯度的交换范畴，然而现有的定义未能涵盖现代数据载体（如合成数据和联邦分析）的全部范围。本文通过提出一个考虑效用和隐私的联邦消息的正式数学定义来解决这一差距。我们引入一种分类法，将这些交换组织为三类：模型结构、统计摘要和数据条件表示。通过基于计算需求、通信成本和隐私风险对这些类别进行评估，我们更清晰地理解了去中心化训练中的权衡。我们对202项近期出版物的回顾显示，自2021年以来，向多样化消息传递范式的显著转变，标志着从标准深度学习更新向更专业化信息共享的迁移。该框架为未来研究提供了一个结构化的路径，以优化联邦系统以适应不同的硬件和安全需求。