Deadlock detection is an essential aspect of concurrency control in parallel and distributed systems, as it ensures the efficient utilization of resources and prevents indefinite delays. This paper presents a comprehensive analysis of the various deadlock detection techniques, including static and dynamic approaches. We discuss the future improvements associated with deadlock detection and provide a comparative evaluation of these techniques in terms of their accuracy, complexity, and scalability. Furthermore, we outline potential future research directions to improve deadlock detection mechanisms and enhance system performance.

Federated learning (FL) is a distributed machine learning (ML) framework where several clients cooperatively train an ML model by exchanging the model parameters without directly sharing their local data. In FL, the limited number of participants for model aggregation and communication latency are two major bottlenecks. Hierarchical federated learning (HFL), with a cloud-edge-client hierarchy, can leverage the large coverage of cloud servers and the low transmission latency of edge servers. There are growing research interests in implementing FL in vehicular networks due to the requirements of timely ML training for intelligent vehicles. However, the limited number of participants in vehicular networks and vehicle mobility degrade the performance of FL training. In this context, HFL, which stands out for lower latency, wider coverage and more participants, is promising in vehicular networks. In this paper, we begin with the background and motivation of HFL and the feasibility of implementing HFL in vehicular networks. Then, the architecture of HFL is illustrated. Next, we clarify new issues in HFL and review several existing solutions. Furthermore, we introduce some typical use cases in vehicular networks as well as our initial efforts on implementing HFL in vehicular networks. Finally, we conclude with future research directions.

Due to the increasing need for massive data analysis and machine learning model training at the network edge, as well as the rising concerns about data privacy, a new distributed training framework called federated learning (FL) has emerged and attracted much attention from both academia and industry. In FL, participating devices iteratively update the local models based on their own data and contribute to the global training by uploading model updates until the training converges. Therefore, the computation capabilities of mobile devices can be utilized and the data privacy can be preserved. However, deploying FL in resource-constrained wireless networks encounters several challenges, including the limited energy of mobile devices, weak onboard computing capability, and scarce wireless bandwidth. To address these challenges, recent solutions have been proposed to maximize the convergence rate or minimize the energy consumption under heterogeneous constraints. In this overview, we first introduce the backgrounds and fundamentals of FL. Then, the key challenges in deploying FL in wireless networks are discussed, and several existing solutions are reviewed. Finally, we highlight the open issues and future research directions in FL scheduling.