Toward an Intelligent Edge: Wireless Communication Meets Machine Learning

Abstract

The recent revival of artificial intelligence (AI) is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and Internet of Things (IoT) devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interests in realizing an "intelligent edge" to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, emerges, which crosses and revolutionizes two disciplines: wireless communication and machine learning. A major theme in edge learning is to overcome the limited computing power, as well as limited data, at each edge device. This is accomplished by leveraging the mobile edge computing (MEC) platform and exploiting the massive data distributed over a large number of edge devices. In such systems, learning from distributed data and communicating between the edge server and devices are two critical and coupled aspects, and their fusion poses many new research challenges. This article advocates a new set of design principles for wireless communication in edge learning, collectively called learning-driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design principles, and unique research opportunities are identified. ! 1Compared with cloud and on-device learning, edge learning has its unique strengths. First, it has the most balanced resource support (see Fig. 1), which helps achieving the best tradeoff between the AI-model complexity and the model-training speed. Second, given its proximity to data sources, edge learning overcomes the drawback of cloud learning that fails to process real-time data due to excessive propagation delay and also network congestion caused by uploading data to the cloud. Furthermore, the proximity gives an additional advantage of location-and-context awareness. Last, compared with on-device learning, edge learning achieves much higher learning accuracy by supporting more complex models and more importantly aggregating distributed data from many devices. Due to the all-rounded capabilities, edge learning can support a wide spectrum of AI models to power a broad range of mission-critical applications, such as autodriving, rescue-operation robots, disaster avoidance and fast industrial control. Nevertheless, edge learning is at its nascent stage and thus remains a largely uncharted area with many open challenges. Fig. 1. Layered in-network machine learning architecture.The main design objective in edge learning is the fast intelligence acquisition from the rich but highly distributed data at subscribed edge devices. This critically depends on data processing at edge servers, as well as efficient communication between edge servers and edge devices. Compared with increasingly high processing speeds at edge servers, communication suffers from hostility of wireless channels (e.g., pathloss, shadowing, and fading), and consequently forms the bottleneck for ultra...

A Crowdsourcing Framework for On-Device Federated Learning

“…In contrast to the above research that has overlooked the participatory method to build a high-quality central ML model and its criticality, and primarily focused on the convergence of learning time with variants of learning algorithms, our work addresses the challenge in designing a communication and computational cost effective FL framework by exploring a crowdsourcing structure. In this regard, few recent studies have discussed about the participation to build a global ML model with FL as in [29], [30]. Basically, in [29] the authors proposed a novel distributed approach based on FL to learn the network-wide queue dynamics in vehicular networks for achieving ultra-reliable low-latency communication (URLLC) via a joint power and resource allocation problem.…”

“…The vehicles participate in FL to provide information related to sample events (i.e., queue lengths) to parameterize the distribution of extremes. In [30], the authors provided new design principles to characterize edge-learning and highlighted important research opportunities and applications with the new philosophy for wireless communication called learning-driven communication. The authors also presented some of the significant case studies and demonstrated the effectiveness of design principles in this regards.…”

“…Algorithm 2 can maintain consensus accuracy by formalizing the clients selection criteria. This is because from (30), z k = 1 for θ k (r) < θ th , and z k = 0 for θ th ≤ θ k (r) < 1. Thus, MEC server uses this setting to drop the participants with θ k (r) > θ * k (r) = θ th .…”

Performance Optimization of Federated Learning over Wireless Networks

“…In [18], the authors developed a federated learning based spiking neural network. However, most of these existing works [7]- [18] whose goal is to minimize the FL convergence time must sacrifice the performance of the FL algorithm. For example, in [11] and [12], the authors sacrificed the training accuracy of FL to improve the convergence time.…”

“…For example, in [11] and [12], the authors sacrificed the training accuracy of FL to improve the convergence time. Moreover, most of these existing works [7]- [18] used random or fixed user selection methods for FL training, which may significantly increase the FL convergence time and also decrease the FL performance. In addition, none of these existing works [7]- [18] considers the effect of the local FL models of the users that cannot connect to the BS due to limited wireless resource on the FL convergence time and performance.…”