联邦学习早就不只传权重了，这篇论文给你梳理了现在的消息类型，分类清楚，还分析了隐私和效率的取舍。
该论文提出了联邦学习消息的正式数学定义，涵盖合成数据和联邦分析等现代负载。作者将联邦消息分为三类：模型结构、统计摘要和数据条件表示，并基于计算开销、通信成本和隐私风险评估了这些类别。通过对202篇近期出版物的回顾，研究发现自2021年以来联邦学习消息范式显著多样化，从标准深度学习更新转向更专业化的信息共享。该框架为优化不同硬件和安全要求的联邦系统提供了结构化路径。
Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages
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.