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New typed tensor language enables shared-state federated computation

By PulseAugur Editorial · [1 sources] ·

A new typed tensor language has been developed to facilitate shared-state federated computations, which combine client-local tensor operations with aggregated shared state. This language separates client-partitioned data from globally available values using two tensor sorts and tracks partitioned axes. It enables typed one-round programs to factor through shared tensors, with encoder components represented by aggregations or contractions and the decoder being shared-only. The system extends to multi-round programs with persistent shared state and supports server-side first-order and curvature-block updates for federated analytics and FedSGD. AI

IMPACT This new language could streamline the development and execution of federated learning models, potentially improving efficiency and scalability in distributed AI training.

RANK_REASON The cluster contains an academic paper detailing a new technical language for a specific type of computation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New typed tensor language enables shared-state federated computation

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The cluster contains an academic paper detailing a new technical language for a specific type of computation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Theofilos Mailis, Theodore Papamarkou, Andreas Ktenidis, Kalliopi-Christina Despotidou, Konstantinos Filippopolitis, Yannis Foufoulas, Thanasis-Michail Karampatsis, Evdokia Mailli, Yannis Ioannidis ·

    A Typed Tensor Language for Shared-State Federated Computation

    arXiv:2605.21103v2 Announce Type: replace Abstract: Shared-state federated computations combine client-local tensor computation, mergeable aggregation into shared state, and shared-only post-processing. We introduce a typed tensor language for this class of computations. Its two …

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