FoMoE partitions expert layers across workers in MoE LLMs, skips non-resident experts, and reports up to 1.42x lower communication than baselines plus 1.4x throughput gains while maintaining stable routing.
Understanding outer optimizers in local SGD: learning rates, momentum, and acceleration
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Local MixVR achieves communication complexity scaling only with number of workers M, independent of total samples N, and outperforms Minibatch Accelerated SGD when M is smaller than order N to the 1/4.
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.
citing papers explorer
-
FoMoE: Breaking the Full-Replica Barrier with a Federation of MoEs
FoMoE partitions expert layers across workers in MoE LLMs, skips non-resident experts, and reports up to 1.42x lower communication than baselines plus 1.4x throughput gains while maintaining stable routing.
-
Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning
Local MixVR achieves communication complexity scaling only with number of workers M, independent of total samples N, and outperforms Minibatch Accelerated SGD when M is smaller than order N to the 1/4.
-
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.