UPMs apply periodic time-varying random invertible transforms to sharded model components in decentralized setups to render cross-time assemblies incoherent while preserving network function and incurring minimal overhead.
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Magnitude of compact R-trees equals 1 + L/2 with L the total length; diversity-maximizing measures concentrate on leaves with no branch points in their support, and max diversity on finite weighted trees is polynomial-time computable.
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Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization
UPMs apply periodic time-varying random invertible transforms to sharded model components in decentralized setups to render cross-time assemblies incoherent while preserving network function and incurring minimal overhead.
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Magnitude and diversity of trees
Magnitude of compact R-trees equals 1 + L/2 with L the total length; diversity-maximizing measures concentrate on leaves with no branch points in their support, and max diversity on finite weighted trees is polynomial-time computable.