Federated learning (FL) has emerged as a promising paradigm for enabling collaborative training of machine learning models while preserving data privacy. However, the massive heterogeneity of data and devices, communication constraints, and security threats pose significant challenges to its practical implementation. This paper provides a system review of the state-of-the-art techniques and future research directions in FL, with a focus on addressing these challenges in resource-constrained environments by a cloud–edge–end collaboration FL architecture. We first introduce the foundations of cloud–edge–end collaboration and FL. We then discuss the key technical challenges. Next, we delve into the pillars of trustworthy AI in the federated context, covering robustness, fairness, and explainability. We propose a dimension reconstruction of trusted AI and analyze the foundations of each trustworthiness pillar. Furthermore, we present a lightweight FL framework for resource-constrained edge–end devices, analyzing the core contradictions and proposing optimization paradigms. Finally, we highlight advanced topics and future research directions to provide valuable insights into the field.

Mobile Crowdsensing (MCS) is a promising paradigm where embedded sensor are exploited for collecting and sharing environmental data. In IoV, participating vehicles sense the environment, collect data and transmit the data to the edge server for processing to provide real-time services. However, real-time services not only pose energy challenges for massive data transmission and analysis, but also demand dynamic multi-timeslot optimisation for MCS systems. Additionally, sensing data often require continuous updates to prevent the provision of obsolete services. To address this, we introduce the concept of Urgency of Information (UoI) to characterize the freshness of sensing data. Diverging from the linear growth trend of Age of Information (AoI), UoI enables dynamic adjustment of the decay rate of data freshness based on traffic complexity. In this paper, we propose a dynamic multi-timeslot MCS system for IoV, under constraints of UoI, which intelligently leverages the spatial correlation of perception to update data and minimizes network energy consumption while ensuring compliance with UoI constraints. Then we propose a Joint Data Collection and Resource Allocation (JDCRA) algorithm to obtain the solution based on convex optimization. To the best of our knowledge, this is the first work to jointly optimize multi-road data collection and resource allocation, taking into account UoI metrics. We evaluate JDCRA by experiments on SUMO in real-world scenarios. Experimental results show that JDCRA outperforms state-of-the-art methods in terms of energy consumption and UoI violation probability, and obtains solutions that consumes only 7.43% more energy than the optimal solution in polynomial complexity.

In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered.