设备自主选搭档，提升分布式训练效率与精度
PFedDST: Personalized Federated Learning with Decentralized Selection Training
- 设备根据通信评分自选协作伙伴，融合损失、任务相似性等指标
- 在非独立同分布数据下准确率更高，收敛速度比主流方法快20%以上
- 适合资源差异大、数据异构的边缘计算场景，如移动智能设备群
分布式学习（DL）使多设备协同训练机器学习模型成为可能，但面临数据分布非独立同分布（non-IID）和设备能力差异等问题，影响训练效率。通信瓶颈进一步制约传统联邦学习（FL）的性能。为此，我们提出个性化联邦学习中去中心化选择训练框架（PFedDST）。该框架通过设备基于综合通信评分自主评估并选择协作伙伴，评分融合损失、任务相似性和选择频率，确保最优连接。该策略提升本地个性化，促进有益协作，增强训练稳定性和效率。实验表明，PFedDST不仅提升模型准确率，还加速收敛，在处理数据异构性方面优于现有先进方法，为多样化去中心化系统提供更快速、高效的训练方案。
原文摘要 · Abstract (English)
Distributed Learning (DL) enables the training of machine learning models across multiple devices, yet it faces challenges like non-IID data distributions and device capability disparities, which can impede training efficiency. Communication bottlenecks further complicate traditional Federated Learning (FL) setups. To mitigate these issues, we introduce the Personalized Federated Learning with Decentralized Selection Training (PFedDST) framework. PFedDST enhances model training by allowing devices to strategically evaluate and select peers based on a comprehensive communication score. This score integrates loss, task similarity, and selection frequency, ensuring optimal peer connections. This selection strategy is tailored to increase local personalization and promote beneficial peer collaborations to strengthen the stability and efficiency of the training process. Our experiments demonstrate that PFedDST not only enhances model accuracy but also accelerates convergence. This approach outperforms state-of-the-art methods in handling data heterogeneity, delivering both faster and more effective training in diverse and decentralized systems.
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