Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages
Quick summary
arXiv:2606.16891v2 Announce Type: replace-cross Abstract: 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
Key takeaways
- arXiv:2606.16891v2 Announce Type: replace-cross Abstract: 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.
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages” may reshape data collection, model training, output accountability and market access.
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