Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients

Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. This approach raises several challenges due to the different statistical distribution of the local datasets and the clients' computational heterogeneity. In particular, the presence of highly non-i.i.d. data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds requested to reach a performance comparable to that of the centralized scenario. As a solution, we propose FedSeq, a novel framework leveraging the sequential training of subgroups of heterogeneous clients, i.e. superclients, to emulate the centralized paradigm in a privacy-compliant way. Given a fixed budget of communication rounds, we show that FedSeq outperforms or match several state-of-the-art federated algorithms in terms of final performance and speed of convergence. Finally, our method can be easily integrated with other approaches available in the literature. Empirical results show that combining existing algorithms with FedSeq further improves its final performance and convergence speed. We test our method on CIFAR-10 and CIFAR-100 and prove its effectiveness in both i.i.d. and non-i.i.d. scenarios.Comment: Published at the 26th International Conference on Pattern Recognition (ICPR), 2022, pp. 3376-338

ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set

This paper introduces an univariate application-independent set of load profiles or time series derived from real-world energy system data. The generation involved a two-step process: manifolding the initial dataset through signal processors to increase diversity and heterogeneity, followed by a declustering process that removes data redundancy. The study employed common feature engineering and machine learning techniques: the time series are transformed into a normalized feature space, followed by a dimensionality reduction via hierarchical clustering, and optimization. The resulting dataset is uniformly distributed across multiple feature space dimensions while retaining typical time and frequency domain characteristics inherent in energy system time series. This data serves various purposes, including algorithm testing, uncovering functional relationships between time series features and system performance, and training machine learning models. Two case studies demonstrate the claims: one focused on the suitability of hybrid energy storage systems and the other on quantifying the onsite hydrogen supply cost in green hydrogen production sites. The declustering algorithm, although a bys study, shows promise for further scientific exploration. The data and source code are openly accessible, providing a robust platform for future comparative studies. This work also offers smaller subsets for computationally intensive research. Data and source code can be found at https://github.com/s-guenther/estss and https://zenodo.org/records/10213145

The Strategides and Themes: A Quantitative Approach to the Byzantine Empire's Administrative Structure

This study interrogates how the Byzantine Empire understood and spatially organized its territorial holdings within Asia Minor between the seventh and eleventh centuries. The objective is to reveal the extent to which administrators understood and utilized geographical principles when addressing large-scale governance challenges. Through the use of geographic information systems (GIS) and quantitative analysis, the study address questions regarding how Byzantine administrators conceptualized the territorial extent of the empire and used factors such as land usage, demographics, and communication constraints to make administrative decisions. Methodologically, this objective is pursued by investigating the spatial composition of the administrative divisions that defined the Byzantine Empire’s territories during this period: the strategides, the themes, and the ducates/katepanates that organized the minor themes. With the support of the extant historical record, there is enough information about the spatial composition of the boundaries, cities, and road networks of these administrative bodies to apply GIS principles and other analytical means to elucidate how these entities functioned. For a period marked by a paucity of extant documentation from the imperial bureaucracy in regards to census figures, land surveys, and itineraria, as well as little stated rationale behind territorial organization, such a study helps to fill an important lacuna in Byzantine administrative history. Within this study is an expansive dataset that provides a resource for future research concerned with the administrative composition of the strategides, themes, and ducates/katepanates. The dataset entails the most detailed and accurate series of maps and tables of the following geographical features related to the strategides, themes, and ducates/katepanates: The territorial boundaries of the strategides, themes, and ducates/katepanates The locations of their capitals and the 386 principal Anatolian cities under their jurisdictions The reconstruction of the more than 34,000 km Byzantine road system within the empire's eastern holdings A network model grounded in geographical determinism that articulates how the themes and Constantinople connected A list of seventy minor themes that allows the themes to be assessed collectively for the first time A heuristic representation of the territorial extents of the minor themes In addition, this study also shows the feasibility of implementing a series of quantitative tests that include: Alpha Indices, area comparisons, betweenness centralities, bivariate and multivariate correlations, Central Place Theory, centroids, clustering coefficients, degree distributions, demographic distributions, heatmaps, isochrone surveys, network connectivity, node-to-node distances, path lengths, satellite overlays, scale-free networks, spatial buffers, and Voronoi diagrams. None of these tests have been implemented previously into a study of the strategides and themes. All of this information is accessible through a robust dataset that can be easily implemented into any future GIS based studies on the strategides, themes, and ducates/katepanates. Data collection is time consuming, so any subsequent GIS studies of the strategides, themes, and ducates/katepanates can use this information as a foundation to quickly implement tests on a variety of quantitative propositions.</p