GAPSL: 一种在异构数据上基于梯度对齐的并行分割学习
GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems
AI总结：
本文提出GAPSL框架，通过梯度对齐和方向一致性增强，提升异构数据下分割学习的训练准确率和延迟。
摘要：
随着神经网络复杂性的增加，对资源受限客户端设备上联邦学习的普及提出了重大挑战。并行分割学习（PSL）通过模型分割将大量计算任务转移到服务器，减少客户端计算负载，并消除客户端模型聚合以降低通信和部署成本。由于PSL无聚合，其训练发散问题源于客户端之间梯度方向不一致。为此，我们提出了GAPSL，一种基于梯度对齐的PSL框架，包含两个关键组件：领导者梯度识别（LGI）和梯度方向对齐（GDA）。LGI动态选择方向一致的客户端梯度以构建领导者梯度，捕捉全局收敛趋势。GDA采用方向感知正则化对齐每个客户端的梯度与领导者梯度，从而缓解设备间梯度方向不一致并增强模型收敛。我们在原型计算测试床上评估了GAPSL。大量实验表明，GAPSL在训练准确率和延迟方面始终优于最先进的基准。
英文摘要：
The increasing complexity of neural networks poses significant challenges for democratizing federated learning (FL) on resource-constrained edge devices. Parallel split learning (PSL) has emerged as a promising solution by offloading substantial computing workload to a server via model partitioning, shrinking client-side computing load, and eliminating the client-side model aggregation for reduced communication and deployment costs. However, the highly heterogeneous nature of client data in edge computing systems causes aggregation-free PSL to suffer from severe training divergence, stemming from gradient directional inconsistency across clients. To address this challenge, we propose GAPSL, a gradient-aligned PSL framework tailored for data-heterogeneous edge systems, which comprises two key components: leader gradient identification (LGI) and gradient direction alignment (GDA). LGI dynamically selects a set of directionally consistent device gradients to construct a leader gradient as a robust proxy for the global convergence trend. GDA employs a direction-aware regularization to align each client's gradient with the leader gradient, thereby mitigating inter-device gradient directional inconsistency and enhancing model convergence. We evaluate GAPSL on a prototype computing testbed. Extensive experiments demonstrate that GAPSL consistently outperforms state-of-the-art benchmarks in training accuracy, convergence latency, and system robustness under severe data heterogeneity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
