arXiv:2606.16891v1 Announce Type: 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 groups b
The rapid evolution of Federated Learning beyond traditional methods necessitates a revised framework to categorize emerging communication protocols and data exchange types, reflecting current technological advancements.
A formal taxonomy of Federated Learning messages provides a critical foundation for standardizing privacy, utility, and interoperability in distributed AI systems, impacting future research and application development.
The understanding of Federated Learning is expanded beyond simple weight/gradient exchanges to include more complex payloads like synthetic data and federated analytics, necessitating new design and security considerations.
- · AI researchers
- · Federated Learning platform developers
- · Privacy-focused AI applications
- · Industries using distributed AI
- · Legacy Federated Learning definitions
- · Systems unprepared for diverse message types
Increased clarity and standardization in Federated Learning research and development.
Faster innovation in privacy-preserving AI and distributed model training across various sectors.
Enhanced ability to leverage disparate and sensitive datasets for AI without centralizing raw information, potentially accelerating specialized AI development.
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Read at arXiv cs.AI