Vehicular edge computing is rapidly amplifying the need to process computation-intensive tasks generated in vehicular environments. Conventional centralized processing frameworks struggle to meet these low-latency demands due to network latency and bandwidth constraints. Federated learning (FL) addresses these challenges by retaining raw data on vehicle nodes (VNs). However, sustainable FL training in vehicular edge computing faces persistent obstacles: the instability of VNs (high entry/exit rates), resource heterogeneity, and the lack of effective incentives. Notably, while existing incentive mechanisms are extensively studied, they inadequately address two critical sustainability barriers: extreme data heterogeneity and the volatile energy costs of mobile VNs. To overcome these challenges, we propose an auction-based sustainable federated learning (ASFL) framework. In this framework, the edge server acts as both the FL task publisher and the auctioneer, while VNs serve as bidders. Each bid encapsulates critical attributes including data quality, computing capacity, and projected energy cost. The core objective of ASFL is to maximize long-term social welfare. Formulating this objective reveals an inherently nonconvex optimization problem. Through rigorous analysis, we derive an equivalent convex formulation. The systematic bidder selection process inherent in ASFL simultaneously mitigates data heterogeneity and promotes rational energy utilization across FL. We theoretically prove that the framework achieves incentive compatibility and individual rationality. Experimental results on MNIST and CIFAR-10 datasets demonstrate the effectiveness of the method in mitigating the impact of non-i.i.d. data and reducing energy consumption.

In mobile edge computing (MEC) networks, federated learning (FL) has emerged as the leading distributed framework for training a shared machine learning model, primarily benefiting from its ability to exchange the information of edge devices (EDs) while safeguarding their privacy. However, in MEC networks, the heterogeneity of communication, computation, and data can result in challenges such as stragglers and data imbalances, thereby impeding the training process of FL. To address these challenges, we propose a Semi-Asynchronous Federated Learning (Semi-AFL) framework with cluster-based scheduling. In Semi-AFL, the EDs can perform local training at their own pace using different stale global models to tackle the straggler effect. Considering the asynchronousity of Semi-AFL and data heterogeneity, we propose a cluster-based scheduling strategy that includes device clustering and device selection. Specifically, it performs clustering based on the label distribution and obtains device-to-cluster information. We further select devices based on clustering information as well as model staleness and contribution, aiming to reduce variance and bias and accelerate model convergence. Experiment results demonstrate the effectiveness of the proposed method in reducing the latency of FL.

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS.