Federated Learning Based Proactive Content Caching in Edge Computing

This is the author accepted manuscript. the final version is available from IEEE via the DOI in this recordContent caching is a promising approach in edge computing to cope with the explosive growth of mobile data on 5G networks, where contents are typically placed on local caches for fast and repetitive data access. Due to the capacity limit of caches, it is essential to predict the popularity of files and cache those popular ones. However, the fluctuated popularity of files makes the prediction a highly challenging task. To tackle this challenge, many recent works propose learning based approaches which gather the users' data centrally for training, but they bring a significant issue: users may not trust the central server and thus hesitate to upload their private data. In order to address this issue, we propose a Federated learning based Proactive Content Caching (FPCC) scheme, which does not require to gather users' data centrally for training. The FPCC is based on a hierarchical architecture in which the server aggregates the users' updates using federated averaging, and each user performs training on its local data using hybrid filtering on stacked autoencoders. The experimental results demonstrate that, without gathering user's private data, our scheme still outperforms other learning-based caching algorithms such as m-epsilon-greedy and Thompson sampling in terms of cache efficiency.Engineering and Physical Sciences Research Council (EPSRC)National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaEuropean Union Seventh Framework Programm

Advances in Learning and Understanding with Graphs through Machine Learning

Graphs have increasingly become a crucial way of representing large, complex and disparate datasets from a range of domains, including many scientific disciplines. Graphs are particularly useful at capturing complex relationships or interdependencies within or even between datasets, and enable unique insights which are not possible with other data formats. Over recent years, significant improvements in the ability of machine learning approaches to automatically learn from and identify patterns in datasets have been made. However due to the unique nature of graphs, and the data they are used to represent, employing machine learning with graphs has thus far proved challenging. A review of relevant literature has revealed that key challenges include issues arising with macro-scale graph learning, interpretability of machine learned representations and a failure to incorporate the temporal dimension present in many datasets. Thus, the work and contributions presented in this thesis primarily investigate how modern machine learning techniques can be adapted to tackle key graph mining tasks, with a particular focus on optimal macro-level representation, interpretability and incorporating temporal dynamics into the learning process. The majority of methods employed are novel approaches centered around attempting to use artificial neural networks in order to learn from graph datasets. Firstly, by devising a novel graph fingerprint technique, it is demonstrated that this can successfully be applied to two different tasks whilst out-performing established baselines, namely graph comparison and classification. Secondly, it is shown that a mapping can be found between certain topological features and graph embeddings. This, for perhaps the the first time, suggests that it is possible that machines are learning something analogous to human knowledge acquisition, thus bringing interpretability to the graph embedding process. Thirdly, in exploring two new models for incorporating temporal information into the graph learning process, it is found that including such information is crucial to predictive performance in certain key tasks, such as link prediction, where state-of-the-art baselines are out-performed. The overall contribution of this work is to provide greater insight into and explanation of the ways in which machine learning with respect to graphs is emerging as a crucial set of techniques for understanding complex datasets. This is important as these techniques can potentially be applied to a broad range of scientific disciplines. The thesis concludes with an assessment of limitations and recommendations for future research

High conductance values in p -folded molecular junctions

Folding processes play a crucial role in the development of function in biomacromolecules. Recreating this feature on synthetic systems would not only allow understanding and reproducing biological functions but also developing new functions. This has inspired the development of conformationally ordered synthetic oligomers known as foldamers. Herein, a new family of foldamers, consisting of an increasing number of anthracene units that adopt a folded sigmoidal conformation by a combination of intramolecular hydrogen bonds and aromatic interactions, is reported. Such folding process opens up an efficient through-space charge transport channel across the interacting anthracene moieties. In fact, single-molecule conductance measurements carried out on this series of foldamers, using the scanning tunnelling microscopy-based break-junction technique, reveal exceptionally high conductance values in the order of 10(-1) G(0) and a low length decay constant of 0.02 angstrom(-1) that exceed the values observed in molecular junctions that make use of through-space charge transport pathways.We thank Dr J.I. Miranda (University of the Basque Country) for assistance on NMR characterization. We are grateful to the Basque Science Foundation for Science (Ikerbasque), POLYMAT, the University of the Basque Country (SGIker and UFI11/23), the Deutsche Forschungsgemeinschaft (AU 373/3-1 and MA 5215/4-1), Gobierno de Espana (Ministerio de Economia y Competitividad CTQ2016-77970-R, CTQ2015-71936-REDT, CTQ2015-71406-ERC, CTQ2015-64579-C3-3P, CTQ-2014-54464-R and CTQ-2015-68148), Gobierno Vasco (BERC programme, Consolidated Groups IT520-10 and PC2015-1-01(06-37)), Diputacion Foral de Guipuzcoa (OF215/2016(ES)), CICECO-Aveiro Institute of Materials, POCI-01-0145-FEDER-007679 (FCT ref. UID/CTM/50011/2013), ON2 (NORTE-07-0162-FEDER-000086) and the European Union (ERA Chemistry, Marie Curie Career Integration Grant No. 618247, and FET-Open (2D-INK) Grant No. 664878)