Clustered federated learning (CFL) is a promising solution to address the performance degradation posed by inconsistent data distribution, enhancing model collaborative training through homogeneous data clustering. However, the dynamic nature of user mobility in mobile edge computing (MEC) scenarios results in highly inconsistent data distributions, further complicating collaborative training. So far, existing CFL schemes rely on a central server for clustering, while the prominent clustering algorithms require a predetermined number of clusters. This inevitably leads to a relatively fixed cluster structure, which fails to adapt to user mobility, thereby hurting the model's performance. To capture the inconsistent data distribution in the dynamic user mobility scenario, this paper proposes a decentralized clustered federated learning framework (FedCVM) based on the voter model, which comprises two modules: an iterative adaptive clustering algorithm (ACAS) and a transition probability-based neighbor aggregation algorithm (TPNA). Specifically, ACAS is designed based on the greedy strategy of minimum spanning forest, which achieves optimal cluster division without predetermining the number of clusters, to realize user dynamic clustering. To further improve the model accuracy, the proposed TPNA sets the optimal exchange weights based on the neighbor interaction principle of the voter model to implement precise updates of similar user parameters. We have conducted the theoretical analysis to demonstrate that FedCVM can select the optimal cluster head and ensure model convergence. Extensive experiments on MNIST, CIFAR-10, DermaMNIST, and HARBox demonstrate that FedCVM consistently achieves the highest accuracy while maintaining a favorable trade-off between accuracy and time cost. Moreover, under simulated mobile scenarios, it yields at least a 5.1% improvement in accuracy and achieves at least a 1.29x reduction in communication overhead compared to SOTA methods. We further validate FedCVM through real-world deployment on a heterogeneous edge testbed, demonstrating its effectiveness in dynamic, decentralized mobile scenarios.

Federated Learning (FL) is a distributed machine learning framework in parallel and distributed systems. However, the systems’ Non-Independent and Identically Distributed (Non-IID) data negatively affect the communication efficiency, since clients with different datasets may cause significant gaps to the local gradients in each communication round. In this article, we propose a Federated Vectorized Averaging (FedVeca) method to optimize the FL communication system on Non-IID data. Specifically, we set a novel objective for the global model which is related to the local gradients. The local gradient is defined as a bi-directional vector with step size and direction, where the step size is the number of local updates and the direction is divided into positive and negative according to our definition. In FedVeca, the direction is influenced by the step size, thus we average the bi-directional vectors to reduce the effect of different step sizes. Then, we theoretically analyze the relationship between the step sizes and the global objective, and obtain upper bounds on the step sizes per communication round. Based on the upper bounds, we design an algorithm for the server and the client to adaptively adjusts the step sizes that make the objective close to the optimum. Finally, we conduct experiments on different datasets, models and scenarios by building a prototype system, and the experimental results demonstrate the effectiveness and efficiency of the FedVeca method.

The growing role artificial intelligence and specifically machine learning is playing in shaping the future of wireless communications has opened up many new and intriguing research directions. This paper motivates the research in the novel direction of vision-aided wireless communications, which aims at leveraging visual sensory information in tackling wireless communication problems. Like any new research direction driven by machine learning, obtaining a development dataset poses the first and most important challenge to vision-aided wireless communications. This paper addresses this issue by introducing the Vision-Wireless (ViWi) dataset framework. It is developed to be a parametric, systematic, and scalable data generation framework. It utilizes advanced 3D-modeling and ray-tracing softwares to generate high-fidelity synthetic wireless and vision data samples for the same scenes. The result is a framework that does not only offer a way to generate training and testing datasets but helps provide a common ground on which the quality of different machine learning-powered solutions could be assessed.

Today in nanometer regime, approximate circuits have attracted much more attention due to the pursuit of low power consumption and high performance. Approximate multiplier is the key arithmetic function in many error-tolerant applications such as signal processing. In this paper, two approximate compressors based on input reordering logic have been proposed for the partial product reduction in the multiplication. A 2-bit reordering circuit is also designed for the proposed multiplier. Experimental results show that the proposed multiplier sacrifices only a small amount of precision (about 0.58%) to drastically reduce power, area, and delay up to (36.6%), (28.8%) and (20.1%) compared to accurate Wallace multiplier. The proposed multiplier achieves a high signal-to-noise ratio (over 50dB) when applied to an image processing algorithm.