ALMPS: A Theoretical Framework for Asynchronous Layer-Wise Mixed-Precision Synchronization in Distributed Deep Learning

Abstract: The escalating complexity of foundation models has rendered distributed training

a necessity, yet the e!ciency of this paradigm is severely constrained by the communication

overhead of gradient synchronization. Traditional synchronous methods (e.g., All-Reduce) and

naive mixed-precision approaches often create bottlenecks that limit scalability and introduce

numerical instability. In this whitepaper, we propose ALMPS (Asynchronous Layer-Wise

Mixed-Precision Synchronization), a novel communication framework designed to decouple

the monolithic gradient exchange into intelligent, layer-centric flows. By dynamically adapting

quantization precision based on layer sensitivity and enabling non-blocking, asynchronous

propagation, ALMPS theoretically maximizes computation-communication overlap. We

present the mathematical formulation of the Adaptive Communication Policy Engine (ACPE)

and the stability-guaranteed update rules, demonstrating a path toward significantly higher

throughput in large-scale cluster environments without compromising model convergence.