UAV-Aided Multi-Community Federated Learning

In this work, we investigate the problem of an online trajectory design for an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several different communities exist, each defined by a unique task to be learned. In this setting, spatially distributed devices belonging to each community collaboratively contribute towards training their community model via wireless links provided by the UAV. Accordingly, the UAV acts as a mobile orchestrator coordinating the transmissions and the learning schedule among the devices in each community, intending to accelerate the learning process of all tasks. We propose a heuristic metric as a proxy for the training performance of the different tasks. Capitalizing on this metric, a surrogate objective is defined which enables us to jointly optimize the UAV trajectory and the scheduling of the devices by employing convex optimization techniques and graph theory. The simulations illustrate the out-performance of our solution when compared to other handpicked static and mobile UAV deployment baselines.Comment: Accepted to be presented at GLOBECOM 2022, IEEE Global Communications Conference: Selected Areas in Communications: Aerial Communications (Globecom 2022 SAC AC)", 4-8 December 2022, Rio de Janeiro, Brazi

Sparser Random Networks Exist: Enforcing Communication-Efficient Federated Learning via Regularization

This work presents a new method for enhancing communication efficiency in stochastic Federated Learning that trains over-parameterized random networks. In this setting, a binary mask is optimized instead of the model weights, which are kept fixed. The mask characterizes a sparse sub-network that is able to generalize as good as a smaller target network. Importantly, sparse binary masks are exchanged rather than the floating point weights in traditional federated learning, reducing communication cost to at most 1 bit per parameter. We show that previous state of the art stochastic methods fail to find the sparse networks that can reduce the communication and storage overhead using consistent loss objectives. To address this, we propose adding a regularization term to local objectives that encourages sparser solutions by eliminating redundant features across sub-networks. Extensive experiments demonstrate significant improvements in communication and memory efficiency of up to five magnitudes compared to the literature, with minimal performance degradation in validation accuracy in some instances.Comment: Draft to be submitte

A national initiative in data science for health: an evaluation of the UK Farr Institute

ObjectiveTo evaluate the extent to which the inter-institutional, inter-disciplinary mobilisation of data and skills in the Farr Institute contributed to establishing the emerging field of data science for health in the UK.&#x0D; Design and Outcome measuresWe evaluated evidence of six domains characterising a new field of science:&#x0D; &#x0D; defining central scientific challenges,&#x0D; demonstrating how the central challenges might be solved,&#x0D; creating novel interactions among groups of scientists,&#x0D; training new types of experts,&#x0D; re-organising universities,&#x0D; demonstrating impacts in society.&#x0D; &#x0D; We carried out citation, network and time trend analyses of publications, and a narrative review of infrastructure, methods and tools.&#x0D; SettingFour UK centres in London, North England, Scotland and Wales (23 university partners), 2013-2018.&#x0D; Results1. The Farr Institute helped define a central scientific challenge publishing a research corpus, demonstrating insights from electronic health record (EHR) and administrative data at each stage of the translational cycle in 593 papers with at least one Farr Institute author affiliation on PubMed. 2. The Farr Institute offered some demonstrations of how these scientific challenges might be solved: it established the first four ISO27001 certified trusted research environments in the UK, and approved more than 1000 research users, published on 102 unique EHR and administrative data sources, although there was no clear evidence of an increase in novel, sustained record linkages. The Farr Institute established open platforms for the EHR phenotyping algorithms and validations (&gt;70 diseases, CALIBER). Sample sizes showed some evidence of increase but remained less than 10% of the UK population in primary care-hospital care linked studies. 3.The Farr Institute created novel interactions among researchers: the co-author publication network expanded from 944 unique co-authors (based on 67 publications in the first 30 months) to 3839 unique co-authors (545 papers in the final 30 months). 4. Training expanded substantially with 3 new masters courses, training &gt;400 people at masters, short-course and leadership level and 48 PhD students. 5. Universities reorganised with 4/5 Centres established 27 new faculty (tenured) positions, 3 new university institutes. 6. Emerging evidence of impacts included: &gt; 3200 citations for the 10 most cited papers and Farr research informed eight practice-changing clinical guidelines and policies relevant to the health of millions of UK citizens.&#x0D; ConclusionThe Farr Institute played a major role in establishing and growing the field of data science for health in the UK, with some initial evidence of benefits for health and healthcare. The Farr Institute has now expanded into Health Data Research (HDR) UK but key challenges remain including, how to network such activities internationally.</jats:p

Case Report Fine needle aspiration cytology of a granular cell tumor arising in the thyroid gland: a case report and review of literature

Abstract: Granular cell tumor (GCT) is an uncommon tumor of soft tissue, and rarely occurs in thyroid. In this article, we report the FNAC results and pathological analysis of a 14-year-old female who presented with a painless mass in the right lobe of thyroid gland. A resection of the right lobe and isthmus of thyroid were applied after cells with abundant strong eosinophilic cytoplasma, indistinct border and inconspicuous nucleolus were found in the FNAC of the mass. Postoperative pathology and immunohistology helped diagnosis the lesion as thyroid GCT. Differential diagnosis from five diseases and cell types were performed and a review of all eleven papers reporting thyroid GCT was provided

Distributed transmitter cooperation and signal processing with limited feedback and backhaul

La coopération des émetteurs est considérée comme une approche prometteuse pour limiter les interférences dans les réseaux sans fil ayant une réutilisation des ressources spectrales très agressive. La coopération des émetteurs permet l'optimisation conjointe de certains paramètres de transmission. Bien que la coopération des émetteurs existe sous différentes formes, une hypothèse commune est le besoin pour les émetteurs entrant en coopération (i) d’acquérir et de partager des informations concernant le canal de propagation ainsi que (ii) d’effectuer une coopération fondée sur les informations diffusées à l'étape précédente. La conception coordonnée des matrices de précodage et, d’une manière encore plus marquée, la transmission conjointe à différents émetteurs sont des exemples importants de méthodes de coopérations présentant ces propriétés. L’acquisition et l'échange de l’information de canal étant strictement limités, il se pose deux questions importantes: (i) Quelle information doit être renvoyée ou échangée de manière à permettre la coopération la plus efficace? (ii) Quelles méthodes permettent de réaliser les gains de la coopération dans ce contexte de partage limité et imparfait d’information? Dans cette thèse, nous abordons les deux questions précédentes. Dans un premier temps, nous considérons que chaque émetteur acquiert une estimée de canal imparfaite. Dans un second temps, nous étudions la conception de techniques de coopération efficaces dans une configuration d’information de canal imparfaitement partagée entre les émetteurs. Enfin, les futures directions de recherche découlant de ces travaux sont présentées et discutées.Transmitter cooperation is considered a promising tool for dealing with interference in wireless networks with an aggressive reuse policy of spectral resources. Although transmitter cooperation comes in many flavors, a recurrent assumption behind proposed methods lies in the need for cooperating devices to (i) acquire, share information pertaining to the propagation channel toward the multiple receivers and (ii) perform cooperation based on the disseminated information in the previous step. This holds true for instance for coordinated beamforming methods and, to an even greater extent, for network-MIMO (Joint Processing coordinated multi-point (JP CoMP) in the long term evolution (LTE) terminology). As feedback and exchange of channel state information (CSI) come at a price in terms of signaling overhead, there arise two important questions: (i) What information should be fed back or exchanged such that the CSI acquired at each transmitter is most informative to perform cooperation? (ii) Which techniques can reap the benefits of cooperation while living with an imperfect channel representation that varies from transmitter to transmitter ? In this thesis, we address both aforementioned questions. We consider first each transmitter acquires an initial imperfect CSI based on limited receivers feedback. For the design of efficient cooperation techniques that copes with the imperfect and non-identical CSI configuration at each transmitter, we investigate specifically a regularized zero forcing (RZF) precoder design in large system scenario. Finally, interesting and challenging research directions and open problems are discussed