Federated Edge Learning (FEEL)
The popularity of mobile devices results in the availability of enormous data and computational resources at the network edge. To leverage the data and resources, a new machine learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing fast and intelligent services to mobile users. While computing speeds are advancing rapidly, the communication latency is becoming the bottleneck of fast edge learning. To address this issue, this work is focused on designing a low latency multi-access scheme for edge learning. We consider a popular framework, federated edge learning (FEEL), where edge-server and on-device learning are synchronized to train a model without violating user-data privacy. It is proposed that model updates simultaneously transmitted by devices over broadband channels should be analog aggregated ‘over-the-air’ by exploiting the superposition property of a multi-access channel. Thereby, ‘interference’ is harnessed to provide fast implementation of the model aggregation. This results in dramatical latency reduction compared with the traditional orthogonal access (i.e., OFDMA). In this work, the performance of FEEL is characterized targeting a single-cell random network. First, due to power alignment between devices as required for aggregation, a fundamental tradeoff is shown to exist between the update-reliability and the expected update-truncation ratio. This motivates the design of an opportunistic scheduling scheme for FEEL that selects devices within a distance threshold. This scheme is shown using real datasets to yield satisfactory learning performance in the presence of high mobility. Second, both the multi-access latency of the proposed analog aggregation and the OFDMA scheme are analyzed. Their ratio, which quantifies the latency reduction of the former, is proved to scale almost linearly with device population. …

Dynamic Author Persona Performed Exceedingly Rapidly (DAPPER)
Extracting common narratives from multi-author dynamic text corpora requires complex models, such as the Dynamic Author Persona (DAP) topic model. However, such models are complex and can struggle to scale to large corpora, often because of challenging non-conjugate terms. To overcome such challenges, in this paper we adapt new ideas in approximate inference to the DAP model, resulting in the Dynamic Author Persona Performed Exceedingly Rapidly (DAPPER) topic model. Specifically, we develop Conjugate-Computation Variational Inference (CVI) based variational Expectation-Maximization (EM) for learning the model, yielding fast, closed form updates for each document, replacing iterative optimization in earlier work. Our results show significant improvements in model fit and training time without needing to compromise the model’s temporal structure or the application of Regularized Variation Inference (RVI). We demonstrate the scalability and effectiveness of the DAPPER model by extracting health journeys from the CaringBridge corpus — a collection of 9 million journals written by 200,000 authors during health crises. …

Excess Risk
In statistics, excess risk is a measure of the relationship between a specified risk factor and a specified outcome (such as contracting a disease). It is the difference between two proportions. In epidemiology it is typically defined to be the difference between the proportion of subjects in a population with a particular disease who were exposed to a specified risk factor and the proportion of subjects with that same disease who were not exposed. …

Data Stewardship
In metadata, a data steward is a person that is responsible for maintaining a data element in a metadata registry. A data steward is a broad job role that incorporates processes, policies, guidelines and responsibilities for administering organizations’ entire data in compliance with business and/or regulatory obligations. A data steward’s responsibility stems from an understanding of the business domain and the interaction of business processes with data entities/elements. A data steward ensures that there are documented procedures and guidelines for data access and use.
A data steward may share some responsibilities with a data custodian, and work with database/warehouse administrators and other related staff to plan and execute an enterprise-wide data governance, control and compliance policy.
Data stewardship roles are common when organizations are attempting to exchange data precisely and consistently between computer systems and reuse data-related resources. Master data management often makes references to the need for data stewardship for its implementation to succeed. …