AI 中文总结
该研究针对分布式边缘网络的海量数据问题，提出F²CTO方法，将分布式鲁棒核心集选择建模为带层级约束的三层优化问题，其收敛速率为O(ε^(-3/2))，经实证验证有效高效。
AI 中文摘要
随着物联网（IoT）的快速发展，分布式边缘网络中产生了海量数据。使用全量数据训练模型会带来显著的计算开销和存储瓶颈，使得核心集选择成为关键范式。此外，考虑到本地数据的隐私敏感性以及实际部署中对模型鲁棒性日益增长的需求，开发用于鲁棒核心集选择的有效分布式优化框架至关重要，但这一方向在很大程度上仍未被探索。为此，本研究首先刻画了核心集选择、鲁棒优化与分布式学习之间的层级依赖关系，并将分布式鲁棒核心集选择问题建模为带层级约束的三层优化问题。此外，为了以分布式方式有效求解该三层问题，提出了联邦一阶约束三层优化（F²CTO），其协同整合了层级复合价值函数重构与分布式交替投影梯度算法。据我们所知，F²CTO是首个为分布式鲁棒核心集选择开发的方法，也是首个用于带层级约束的三层优化问题的分布式优化方法。此外，我们证明所提方法在寻找ε-驻点时达到了O(ε^(-3/2))的非渐近收敛速率。在可靠持续学习上的大量实证评估表明了所提F²CTO的有效性与效率。
英文摘要
With the rapid advancement of the Internet of Things (IoT), massive amounts of data are generated across distributed edge networks. Training models on full data incurs significant computational overhead and storage bottlenecks, rendering coreset selection a critical paradigm. Furthermore, given the privacy-sensitive nature of local data and the escalating demand for model robustness in real-world deployments, developing an effective distributed optimization framework for robust coreset selection is vital, yet remains largely unexplored. To this end, this work first characterizes the hierarchical dependencies among coreset selection, robust optimization, and distributed learning, and formulates the distributed robust coreset selection as a trilevel optimization problem with level-wise constraints. Furthermore, to effectively solve the trilevel problem in a distributed manner, the \underline{F}ederated \underline{F}irst-order \underline{C}onstrained \underline{T}rilevel \underline{O}ptimization (F$^2$CTO) is proposed, which synergistically integrates a hierarchical composite value-function reformulation and a distributed alternating projected gradient algorithm. To the best of our knowledge, F$^2$CTO is the first method developed for distributed robust coreset selection, as well as the first distributed optimization approach for trilevel optimization problems with level-wise constraints. Additionally, we prove that the proposed method achieves a non-asymptotic convergence rate of $\mathcal{O}(ε^{-3/2})$ for finding an $ε$-stationary point. Extensive empirical evaluations on reliable continual learning demonstrate the effectiveness and efficiency of the proposed F$^2$CTO.
