Chameleon: Adaptive Bidirectional Pipeline Parallelism for Edge Collaborative Training

Abstract

The shift of deep learning (DL) from the cloud to the edge introduces a critical systems challenge: enabling efficient and stable distributed training under dynamic and heterogeneous conditions. Pipeline parallelism is crucial for training throughput, but existing frameworks, designed for cloud servers, incur high overhead and adapt poorly to dynamic, heterogeneous edges. To bridge this gap, we propose Chameleon, a synchronous and adaptive bidirectional pipeline framework that achieves high throughput in dynamic and heterogeneous edge systems. At its core, Chameleon incorporates a hardware-aware throughput model to capture pipeline stage performance on heterogeneous devices. This model enables a stage-adaptive runtime mechanism, featuring a scheduler that mitigates contention through dynamic task scheduling and a micro-batch optimizer that tunes per-stage batch sizes using real-time throughput feedback. Extensive experiments show that Chameleon outperforms state-of-the-art methods, improving training throughput by up to 2.1 × in dynamic and heterogeneous edge systems.