发表机构
Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对任务关系未知的网络场景，提出从分布式数据学习任务图的分散式两阶段策略，实现合作式多任务扩散学习，性能优于非合作学习且逼近真实图基线。
AI 中文摘要
本文研究了当底层任务关系未知时，网络上的分散式多任务学习问题。现有的图正则化多任务框架通常假设结构已知，但实际场景往往需要从分布式数据中直接学习任务间的依赖关系。我们提出一种分散式两阶段策略：首先从带噪声的非合作随机梯度迭代中估计广义图拉普拉斯矩阵，随后利用学习到的图实现合作式多任务扩散学习。该框架由高斯马尔可夫随机场先验驱动，由此得到图拉普拉斯矩阵的分散式最大似然估计器。分析量化了拉普拉斯估计误差及其向多任务扩散递归稳态性能的传播，并引入拓扑敏感性指数以捕捉网络异质性的影响。仿真结果证实了理论发现，表明由学习到的任务图实现的合作机制相比非合作学习显著提升性能，且当估计步长足够小时，可逼近真实图的基线性能。
英文摘要
This paper investigates decentralized multitask learning over networks when the underlying task relationships are unknown. While existing graph-regularized multitask frameworks typically assume a known structure, practical settings often require learning inter-task dependencies directly from distributed data. We propose a decentralized two-phase strategy that first estimates a generalized graph Laplacian from noisy non-cooperative stochastic gradient iterates, and subsequently exploits the learned graph to enable cooperative multitask diffusion learning. This framework is motivated by a Gaussian Markov random field prior, which gives rise to a decentralized maximum likelihood estimator for the graph Laplacian. The analysis quantifies the Laplacian estimation error and its propagation to the steady-state performance of the multitask diffusion recursion, and introduces a topology sensitivity index to capture the effect of network heterogeneity. Simulation results corroborate the theoretical findings and demonstrate that cooperation enabled by the learned task graph significantly improves performance over non-cooperative learning, while approaching the true-graph baseline when the estimation stepsize is sufficiently small.
