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cs.LG updates on arXiv.org

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A Typed Tensor Language for Federated Learning
Theofilos Ma · 2026-05-21 · via cs.LG updates on arXiv.org

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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 tensor, used only as a reference object. The main result is a shared-state factorization theory. We show that typed one-round programs factor through fixed-dimensional shared state whose size is independent of the number of clients and records, computed from client-local tensor expressions and merged across clients. We also prove a converse representability result; factorizations whose encoders and decoders are expressible in the language are realized by typed one-round programs, and the correspondence extends to iterative programs whose cross-round state is shared. This gives a formal account of the computations in the language that can be expressed as encode, merge, and decode procedures. We then develop a differentiable fragment for learning. If a per-record loss and its per-record gradient are represented by client-local tensor expressions, the global gradient is represented by record-axis summation of the federated gradient tensor. This yields typed iterative programs for server-side gradient descent and shared-linear-algebra second-order updates. The framework characterizes a broad class of federated learning computations whose communication passes through fixed-dimensional shared state.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2605.21103 [cs.LG]
  (or arXiv:2605.21103v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2605.21103

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Theodore Papamarkou [view email]
[v1] Wed, 20 May 2026 12:34:24 UTC (49 KB)

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