Learning generative models is challenging for a network edge node with limited data and computing power. Since tasks in similar environments share a model similarity, it is plausible to leverage pretrained generative models from other edge nodes. Appealing to optimal transport theory tailored toward Wasserstein-1 generative adversarial networks (WGANs), this study aims to develop a framework that systematically optimizes continual learning of generative models using local data at the edge node while exploiting adaptive coalescence of pretrained generative models. Specifically, by treating the knowledge transfer from other nodes as Wasserstein balls centered around their pretrained models, continual learning of generative models is cast as a constrained optimization problem, which is further reduced to a Wasserstein-1 barycenter problem. A two-stage approach is devised accordingly: 1) the barycenters among the pretrained models are computed offline, where displacement interpolation is used as the theoretic foundation for finding adaptive barycenters via a "recursive" WGAN configuration and 2) the barycenter computed offline is used as metamodel initialization for continual learning, and then, fast adaptation is carried out to find the generative model using the local samples at the target edge node. Finally, a weight ternarization method, based on joint optimization of weights and threshold for quantization, is developed to compress the generative model further. Extensive experimental studies corroborate the effectiveness of the proposed framework.

Though electric vehicles (EVs) are efficient in power consumption, EV charging is time consuming and hence EV users may experience long delay for charging during peak hours in urban areas. Reshaping of heterogeneous EV charging demand enhances user experience and charging stations' profit. This study proposes a demand reshaping framework, in which each charging station announces different hourly charging prices ahead of time and EV users can freely select their charging destinations. The optimal charging prices should minimize the waiting duration for charging and maximize charging stations' profit. To this end, charging stations train a deep neural network model to predict hourly charging demand at distinct charging stations. Subsequently, the optimal prices are numerically computed by leveraging the trained neural network. We show that peak demand for EV charging is smoothed out both spatially and tem-porarily for improved quality of service via monetary incentives. Consequently, EV users benefit from decreased charging duration and charging stations obtain profit from increased service quality.

The electric vehicle (EV) fleet is gradually growing into a major part of public transportation. Proper planning and operation of EV supply equipment (EVSE) is essential to ensure the efficient and economic operations of the EV fleets. Charging stations (CS) have gained market attention due to their lower cost and versatility. Battery swapping stations (BSS) have also received considerable attention because of their promise to provide fast and sustainable battery replacements. However, their commercial viability is unclear due to their requirement for large capital and infrastructure deployment. In this paper, we develop a stochastic model for interactions between CS/BSS and taxi/bus fleets. The model is based on a realistic abstraction of users’ behavior defined by various stochastic processes. It also considers the dynamic impacts of the road congestion. Analytical revenue boundaries are derived and verified by simulations. These simulation results may prove valuable for future studies of public transit.

We consider a many-to-one wireless architecture for federated learning at the network edge, where multiple edge devices collaboratively train a model using local data. The unreliable nature of wireless connectivity, together with constraints in computing resources at edge devices, dictates that the local updates at edge devices should be carefully crafted and compressed to match the wireless communication resources available and should work in concert with the receiver. Thus motivated, we propose SGD-based bandlimited coordinate descent algorithms for such settings. Specifically, for the wireless edge employing over-the-air computing, a common subset of k-coordinates of the gradient updates across edge devices are selected by the receiver in each iteration, and then transmitted simultaneously over k sub-carriers, each experiencing time-varying channel conditions. We characterize the impact of communication error and compression, in terms of the resulting gradient bias and mean squared error, on the convergence of the proposed algorithms. We then study learning-driven communication error minimization via joint optimization of power allocation and learning rates. Our findings reveal that optimal power allocation across different sub-carriers should take into account both the gradient values and channel conditions, thus generalizing the widely used water-filling policy. We also develop sub-optimal distributed solutions amenable to implementation.

Online meta-learning is emerging as an enabling technique for achieving edge intelligence in the IoT ecosystem. Nevertheless, to learn a good meta-model for within-task fast adaptation, a single agent alone has to learn over many tasks, and this is the so-called 'cold-start' problem. Observing that in a multi-agent network the learning tasks across different agents often share some model similarity, we ask the following fundamental question: "Is it possible to accelerate the online meta-learning across agents via limited communication and if yes how much benefit can be achieved?" To answer this question, we propose a multi-agent online meta-learning framework and cast it as an equivalent two-level nested online convex optimization (OCO) problem. By characterizing the upper bound of the agent-task-averaged regret, we show that the performance of multi-agent online meta-learning depends heavily on how much an agent can benefit from the distributed network-level OCO for meta-model updates via limited communication, which however is not well understood. To tackle this challenge, we devise a distributed online gradient descent algorithm with gradient tracking where each agent tracks the global gradient using only one communication step with its neighbors per iteration, and it results in an average regret O(T/N) per agent, indicating that a factor of 1/N speedup over the optimal single-agent regret O(T) after T iterations, where N is the number of agents. Building on this sharp performance speedup, we next develop a multi-agent online meta-learning algorithm and show that it can achieve the optimal task-average regret at a faster rate of O(1 N/T) via limited communication, compared to single-agent online meta-learning. Extensive experiments corroborate the theoretic results.

The past few years have witnessed the explosive growth of Internet of Things (IoT) devices. The necessity of real-time edge intelligence for IoT applications demands that decision making must take place right here right now at the network edge, thus dictating that a high percentage of the IoT created data should be stored and analyzed locally. However, the computing resources are constrained and the amount of local data is often very limited at edge nodes. To tackle these challenges, we propose a distributionally robust optimization (DRO)-based edge intelligence framework, which is based on an innovative synergy of cloud knowledge transfer and local learning. More specifically, the knowledge transfer from the cloud learning is in the form of a reference distribution and its associated uncertainty set. Further, based on its local data, the edge device constructs an uncertainty set centered around its empirical distribution. The edge learning problem is then cast as a DRO problem subject to the above two distribution uncertainty sets. Building on this framework, we investigate two problem formulations for DRO-based edge intelligence, where the uncertainty sets are constructed using the Kullback-Leibler divergence and the Wasserstein distance, respectively. Numerical results demonstrate the effectiveness of the proposed DRO-based framework.

In a dynamic network environment, the applicability of traffic engineering techniques requires fresh traffic measurements, fast routing solvers and frequent network reconfigurations. However, the ages of traffic measurements exhibit significant variation due to asynchronization and random communication delays between routers and controllers. Besides, frequent reconfigurations may incur routing instability, and hence impair network utilization. We devise a controller-assisted distributed routing scheme with recursive link weight reconfigurations, accounting for the impact of measurement ages and routing instability. In particular, the controller estimates the current traffic conditions using an autoregressive model to account for the uncertainty of the age of measurements. A fast load-sensitive link weight update algorithm swiftly computes a new set of OSPF weights by using the estimated link loads. To reduce complexity, a myopic policy is used to determine link weight reconfiguration, which takes into consideration congestion, measurement ages, and possible instability. Since distributed routing offers stronger robustness against link failures compared to centralized routing, the proposed adaptive routing approach offers desirable robustness and further benefits from the controller assistance via iterative search of better OSPF weights.