Abstract--Addressing the problem of Age of Information (AoI) deterioration caused by packet collisions and vehicle speed-related channel uncertainties in Semi-Persistent Scheduling (SPS) for the Internet of V ehicles (IoV), this letter proposes an optimization approach based on Large Language Models (LLM) and Deep Deterministic Policy Gradient (DDPG). First, an AoI calculation model influenced by vehicle speed, vehicle density, and Resource Reservation Interval (RRI) is established, followed by the design of a dual-path optimization scheme. The DDPG is guided by the state space and reward function, while the LLM leverages contextual learning to generate optimal parameter configurations. Experimental results demonstrate that LLM can significantly reduce AoI after accumulating a small number of exemplars without requiring model training, whereas the DDPG method achieves more stable performance after training. HE Internet of V ehicles (IoV) is pivotal in enabling intelligent transportation systems [1], [2], [3].

Federated learning (FL) has emerged as a promising distributed learning paradigm for training deep neural networks (DNNs) at the wireless edge, but its performance can be severely hindered by unreliable wireless transmission and inherent data heterogeneity among clients. Existing solutions primarily address these challenges by incorporating wireless resource optimization strategies, often focusing on uplink resource allocation across clients under the assumption of homogeneous client-server network standards. However, these approaches overlooked the fact that mobile clients may connect to the server via diverse network standards (e.g., 4G, 5G, Wi-Fi) with customized configurations, limiting the flexibility of server-side modifications and restricting applicability in real-world commercial networks. This paper presents a novel theoretical analysis about how transmission failures in unreliable networks distort the effective label distributions of local samples, causing deviations from the global data distribution and introducing convergence bias in FL. Our analysis reveals that a carefully designed client selection strategy can mitigate biases induced by network unreliability and data heterogeneity . Motivated by this insight, we propose FedCote, a client selection approach that optimizes client selection probabilities without relying on wireless resource scheduling. Experimental results demonstrate the robustness of FedCote in DNN-based classification tasks under unreliable networks with frequent transmission failures. With rapid advancements in mobile communications and artificial intelligence (AI), edge AI, which leverages locally generated data to train deep neural networks (DNNs) at the wireless edge, has gained significant attention from both academia and industry [1], [2], [3], [4]. A prominent approach in this domain is federated learning (FL), where an edge server coordinates mobile clients in collaboratively training a shared DNN model while ensuring client privacy [5], [6], [7]. However, FL faces a critical challenge due to ubiquitous data heterogeneity across clients, where training data are distributed in a non-i.i.d. and unbalanced manner. If not addressed, data heterogeneity can severely degrade FL performance [8], [9], [10], [11], [12]. Numerous FL algorithms have been proposed to mitigate this issue. For example, FedProx [13] introduced a regularization term in the local objective function to control model divergence, while SCAFFOLD [14] employed control variates to correct local model drift. HFMDS [15] learned essential class-relevant features of real samples to generate an auxiliary synthetic dataset, which was shared among clients for local training, helping to alleviate data heterogeneity .