Echelon enables auditable aggregate-only adaptation of language models across privacy boundaries by training locally and sharing only boundary-level aggregates, achieving competitive performance in 1B LoRA experiments.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Periodic outer-momentum restarts in two-phase optimizers exploit phase cancellation in a linearized NTK model to widen stable learning-rate and momentum ranges in language-model pretraining.
Distributed training may enable evasion of cluster-based compute governance for frontier AI, requiring new detection approaches such as chip tracking and cluster thresholds.
citing papers explorer
-
Echelon: Auditable Aggregate-Only Language-Model Adaptation Across Privacy Boundaries
Echelon enables auditable aggregate-only adaptation of language models across privacy boundaries by training locally and sharing only boundary-level aggregates, achieving competitive performance in 1B LoRA experiments.
-
Outer-Momentum Restarting in High-Dimensional Two-Phase Optimization
Periodic outer-momentum restarts in two-phase optimizers exploit phase cancellation in a linearized NTK model to widen stable learning-rate and momentum ranges in language-model pretraining.
-
Does Distributed Training Undermine Compute Governance?
Distributed training may enable evasion of cluster-based compute governance for frontier AI, requiring new detection approaches such as chip tracking and cluster thresholds.