发表机构
Durham University; University of Cambridge（杜伦大学; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对非独立同分布数据下分散式学习的拓扑敏感问题，提出本地优先启发式进化（LFHE）框架，仅用局部信息进行受限拓扑搜索，在四个基准上取得竞争性能，并揭示性能与局部性的权衡。
AI 中文摘要
分散式学习在非独立同分布数据下对通信拓扑高度敏感。自适应对等选择方法可以利用本地模型信息，但更广泛的对等发现可能需要越来越大的控制状态，而直接的光谱优化通常依赖于图级信息。我们研究了受限局部拓扑搜索的中间设置，并提出了本地优先启发式进化（LFHE），这是一种表示驱动的重连框架，其候选发现和评分仅使用自我邻域和朋友的朋友（FoF）信息。结构评分通过图狄利克雷能量具有精确的解释：其在客户端上的总和等于表示狄利克雷能量的两倍，在标准线性共识动态下，该能量控制表示分歧的瞬时耗散。LFHE将此状态相关的结构信号与早期探索和度数控制相结合，而代数连通性仍作为离线图诊断。在受限稀疏度数下，其FoF候选状态保持局部性，而非扩展至群体范围的对等跟踪。在四个图像、语音和文本基准上，LFHE实现了具有竞争力的分散式学习性能。匹配协议的控制实验确定结构项是主要的经验拓扑选择信号，而与更广泛对等发现的比较则揭示了预测性能与发现状态局部性之间的权衡。这些结果共同激励了在对等选择与全局信息拓扑优化之间进行状态感知的受限局部拓扑搜索。
英文摘要
Decentralized learning is highly sensitive to communication topology under non-IID data. Adaptive peer-selection methods can exploit local model information, but broader peer discovery may require increasingly large control state, whereas direct spectral optimization typically relies on graph-wide information. We study the intermediate setting of bounded local topology search and propose Local-First Heuristic Evolution (LFHE), a representation-driven rewiring framework whose candidate discovery and scoring use only ego-neighborhood and friend-of-a-friend (FoF) information. The structural score admits an exact interpretation through graph Dirichlet energy: its sum across clients equals twice the representation Dirichlet energy, which under standard linear consensus dynamics governs the instantaneous dissipation of representation disagreement. LFHE combines this state-dependent structural signal with early exploration and degree control, while algebraic connectivity remains an offline graph diagnostic. Under bounded sparse degree, its FoF candidate state remains local rather than expanding toward population-wide peer tracking. Across four image, speech, and text benchmarks, LFHE achieves competitive decentralized learning performance. Matched-protocol controls identify the structural term as the principal empirical topology-selection signal, while comparison with broader peer discovery exposes a trade-off between predictive performance and discovery-state locality. Together, these results motivate state-aware bounded local topology search between pairwise peer selection and globally informed topology optimization.
CommentsPreprint. 31 pages, 12 figures, 8 tables
