arXiv:2605.21103v1 Announce Type: new Abstract: Federated learning and analytics are often described as collections of separate protocols, even when they share the same mathematical form: client-local tensor computation, mergeable aggregation into shared state, and shared-only post-processing. We introduce a typed tensor language that formalizes this structure. The language distinguishes federated tensors, whose records are partitioned across clients along a tracked record axis, from shared tensors, which are available globally. Its semantics are defined by comparison with a virtual global ten
The increasing complexity and privacy demands of distributed AI systems like federated learning necessitate new formalisms to ensure correctness and efficiency.
This development is important for enabling more robust, secure, and privacy-preserving AI development, particularly in sensitive sectors, by providing a structured approach to federated learning.
The formalization of federated learning into a typed tensor language provides a standardized and verifiable framework for its implementation, potentially accelerating its adoption and ensuring its reliability.
- · AI developers
- · Privacy-focused industries
- · Distributed computing platforms
- · Federated learning researchers
- · Ad-hoc federated learning implementations
- · Organizations with poor data governance
Increased adoption of federated learning in new applications due to improved reliability and formal guarantees.
Reduced data privacy risks and enhanced regulatory compliance for AI systems operating on distributed data.
Acceleration of edge AI applications where data must remain local, fostering innovation in sensitive domains like healthcare and finance.
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