New typed tensor language formalizes federated learning structure

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Researchers have developed a typed tensor language to formalize the structure of federated learning and analytics. This language distinguishes between federated tensors partitioned across clients and shared tensors available globally. A key finding is a shared-state factorization theory, demonstrating that one-round federated programs can be factored through fixed-dimensional shared state independent of client count. AI

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IMPACT Introduces a formal framework for federated learning computations, potentially improving efficiency and understanding of communication protocols.

RANK_REASON The cluster contains an academic paper detailing a new formal language for federated learning. [lever_c_demoted from research: ic=1 ai=1.0]

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Theodore Papamarkou

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arXiv cs.LG TIER_1 · Yannis Ioannidis · 2026-05-20 12:34

A Typed Tensor Language for Federated Learning

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 langu…

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A Typed Tensor Language for Federated Learning

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