Federated edge learning (FEEL) has recently emerged as a promising paradigm for achieving edge intelligence (EI) via enabling collaborative model training across edge devices while protecting data privacy. In this paper, we put forth an online optimization framework that jointly manages federated training and inference on resource-constrained edge devices. We introduce a tandem-queue-inspired conversion mechanism that bridges inference requests and training data, and further incorporate both data and model freshness into the accuracy formulation to capture temporal dynamics in real-world environments. To maximize inference accuracy while minimizing latency and energy consumption, the mode selections, communication, and computation resource allocations of edge devices are jointly optimized. We formulate this optimization as a multi-objective optimization problem, which is NP-hard and further complicated by the online setting. To address these challenges, we transform the problem into a multi-objective Markov decision process (MOMDP) and develop a \underline{c}onstrained \underline{m}ulti-\underline{o}bjective \underline{p}roximal \underline{p}olicy \underline{o}ptimization (C-MOPPO) algorithm. Specifically, C-MOPPO first learns a set of policies with different preferences across three objectives, then leverages constrained policy optimization to enrich the Pareto front and obtain high-quality, dense solutions. Extensive experiments demonstrate that C-MOPPO achieves well-balanced trade-offs among objectives and significantly outperforms baselines under various system configurations.

翻译：联邦边缘学习（FEEL）近期已成为实现边缘智能（EI）的一种有前景的范式，它通过支持边缘设备上的协作模型训练同时保护数据隐私。本文提出了一种在线优化框架，用于在资源受限的边缘设备上联合管理联邦训练与推理。我们引入了一种基于串联队列的转换机制，将推理请求与训练数据相连接，并进一步将数据新鲜度和模型新鲜度纳入精度公式，以捕捉真实环境中的时间动态特性。为在最小化延迟和能耗的同时最大化推理精度，边缘设备的模式选择、通信与计算资源分配被联合优化。我们将此优化问题建模为多目标优化问题，其NP-hard特性以及在线设置进一步增加了求解难度。为应对这些挑战，我们将问题转化为多目标马尔可夫决策过程（MOMDP），并提出了一种约束多目标近端策略优化（C-MOPPO）算法。具体而言，C-MOPPO首先学习一组具有不同目标偏好策略，然后利用约束策略优化来丰富帕累托前沿，获得高质量且密集的解。大量实验表明，C-MOPPO能在多个目标之间实现良好平衡的权衡，并在各种系统配置下显著优于基线方法。