针对联邦元学习中个性化需求不足问题，提出基于学习价值的优化框架。
Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems
- 引入学习价值（VoL）衡量设备个体训练需求，动态分配资源。
- 设计任务级权重（TLW）机制，在公平性与任务重要性间取得平衡。
- 采用深度Q网络求解复杂优化问题，适用于非正交多址网络场景。
联邦学习（FL）因分布式架构和隐私保护优势备受关注，但传统方法仅共享全局模型，难以满足多样化任务需求。联邦元学习（FML）通过服务器下发元模型，使设备可本地微调，提升个性化能力。本文提出一种面向非正交多址（NOMA）网络的任务导向型FML框架，引入学习价值（VoL）度量各设备的个体训练需求，并定义任务级权重（TLW）以兼顾任务重要性与公平性，指导边缘设备在训练中的优先级。目标是最大化基于TLW的总学习价值，形成非凸混合整数非线性规划（MINLP）问题，采用参数化深度Q网络（PDQN）算法联合处理离散与连续变量。仿真结果表明，该方法显著优于基线方案，验证了框架的有效性。
原文摘要 · Abstract (English)
Federated Learning (FL) has gained significant attention in recent years due to its distributed nature and privacy preserving benefits. However, a key limitation of conventional FL is that it learns and distributes a common global model to all participants, which fails to provide customized solutions for diverse task requirements. Federated meta-learning (FML) offers a promising solution to this issue by enabling devices to finetune local models after receiving a shared meta-model from the server. In this paper, we propose a task-oriented FML framework over non-orthogonal multiple access (NOMA) networks. A novel metric, termed value of learning (VoL), is introduced to assess the individual training needs across devices. Moreover, a task-level weight (TLW) metric is defined based on task requirements and fairness considerations, guiding the prioritization of edge devices during FML training. The formulated problem, to maximize the sum of TLW-based VoL across devices, forms a non-convex mixed-integer non-linear programming (MINLP) challenge, addressed here using a parameterized deep Q-network (PDQN) algorithm to handle both discrete and continuous variables. Simulation results demonstrate that our approach significantly outperforms baseline schemes, underscoring the advantages of the proposed framework.
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