User Selection Approaches to Mitigate the Straggler Effect for Federated Learning on Cell-Free Massive MIMO Networks

This work proposes UE selection approaches to mitigate the straggler effect for federated learning (FL) on cell-free massive multiple-input multiple-output networks. To show how these approaches work, we consider a general FL framework with UE sampling, and aim to minimize the FL training time in this framework. Here, training updates are (S1) broadcast to all the selected UEs from a central server, (S2) computed at the UEs sampled from the selected UE set, and (S3) sent back to the central server. The first approach mitigates the straggler effect in both Steps (S1) and (S3), while the second approach only Step (S3). Two optimization problems are then formulated to jointly optimize UE selection, transmit power and data rate. These mixed-integer mixed-timescale stochastic nonconvex problems capture the complex interactions among the training time, the straggler effect, and UE selection. By employing the online successive convex approximation approach, we develop a novel algorithm to solve the formulated problems with proven convergence to the neighbourhood of their stationary points. Numerical results confirm that our UE selection designs significantly reduce the training time over baseline approaches, especially in the networks that experience serious straggler effects due to the moderately low density of access points.Comment: submitted for peer review

The Significance of Energy Storage for Renewable Energy Generation and the Role of Instrumentation and Measurement

International audienceEnergy storage is not a new concept but is currently getting increasing importance in the context of energy transition paradigm. Indeed, it is expected to play a key role as an enabling technology for lowering the carbon footprint of the electric power system. In fact, the growing development of renewable energy resources and their increasing share in the energy mix, are introducing significant challenges to the existing power grid due to the high variability of these sources/loads. In particular, maintaining the generation-consumption balance of the electric power in real time, as well as the overall power system security, when these special energy sources/loads are present at a significant scale is a major concern. With competitive energy storage, it will be possible to introduce more flexibility in the electrical system thus helping it to better manage the overall energy balance with better system response in case of severe contingencies. Energy storage technologies were historically used for managing the load curve while observing generation dynamic constraints. The most well-known storage technology is the pumped hydro storage where the energy is stored in a hydraulic form (water potential energy). With the event of open access and the corresponding unbundling of electric power industry segments, valorizing energy storage options under market conditions has become tricky. The major present barriers for deploying energy storage systems (ESS) are high cost, competitive economic value, efficiency and energy density, together with energy policies. The new energy paradigm has put a new emphasis on energy storage, and many research roadmaps have pointed out the need for overcoming the current barriers. The decision makers' awareness of the importance of energy storage is also on the rise. However, adequate incentives for encouraging massive deployment of ESS and storage technology within the electric power system are still lacking. Currently, most of the effort is dedicated to in situ demonstration projects in striving for smarter grids and support of innovations with the corresponding proofs of concept and feedback experience. Additionally, different grid applications are assessed for both centralized to decentralized uses. Various energy storage applications for frequency regulation, voltage support, investment optimization, or peak shaving are under consideration. In this article, some of the main energy storage technologies will be reviewed according to their main application domains. That will be followed by a focus on battery energy storage. Some key elements of battery management system (BMS) technologies and ESS architecture and characterization will be addressed. Then some aspects of ESS protection will be presented and the key trends and indications of emerging concepts for energy storage will be identified

An overview of grid-edge control with the digital transformation

Distribution networks are evolving to become more responsive with increasing integration of distributed energy resources (DERs) and digital transformation at the grid edges. This evolution imposes many challenges to the operation of the network, which then calls for new control and operation paradigms. Among others, a so-called grid-edge control is emerging to harmonise the coexistence of the grid control system and DER’s autonomous control. This paper provides a comprehensive overview of the grid-edge control with various control architectures, layers, and strategies. The challenges and opportunities for such an approach at the grid edge with the integration of DERs and digital transformation are summarised. The potential solutions to support the network operation by using the inherent controllability of DER and the availability of the digital transformation at the grid edges are discussed

Network alignment across social networks using multiple embedding techniques

Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be one of the most important research directions in the field of social network analysis. There are many different models for finding users that overlap between two networks, but most of these models use separate and different techniques to solve prediction problems, with very little work that has combined them. In this paper, we propose a method that combines different embedding techniques to solve the network alignment problem. Each association network alignment technique has its advantages and disadvantages, so combining them together will take full advantage and can overcome those disadvantages. Our model combines three-level embedding techniques of text-based user attributes, a graph attention network, a graph-drawing embedding technique, and fuzzy c-mean clustering to embed each piece of network information into a low-dimensional representation. We then project them into a common space by using canonical correlation analysis and compute the similarity matrix between them to make predictions. We tested our network alignment model on two real-life datasets, and the experimental results showed that our method can considerably improve the accuracy by about 10-15% compared to the baseline models. In addition, when experimenting with different ratios of training data, our proposed model could also handle the over-fitting problem effectively.Web of Science1021art. no. 397