Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.
Abstract. We release two datasets that track the enhanced landsliding induced by the
2008 Mw 7.9 Wenchuan earthquake over a portion of the Longmen
Mountains, at the eastern margin of the Tibetan Plateau (Sichuan, China). The
first dataset is a geo-referenced multi-temporal polygon-based inventory of
pre- and coseismic landslides, post-seismic remobilisations of coseismic
landslide debris and post-seismic landslides (new failures). It covers
471 km2 in the earthquake's epicentral area, from 2005 to 2018. The
second dataset records the debris flows that occurred from 2008 to 2017 in a
larger area (∼17 000 km2), together with information on their
triggering rainfall as recorded by a network of rain gauges. For some
well-monitored events, we provide more detailed data on rainfall, discharge,
flow depth and density. The datasets can be used to analyse, on various
scales, the patterns of landsliding caused by the earthquake. They can be
compared to inventories of landslides triggered by past or new earthquakes or
by other triggers to reveal common or distinctive controlling factors. To our
knowledge, no other inventories that track the temporal evolution of
earthquake-induced mass wasting have been made freely available thus far. Our
datasets can be accessed from https://doi.org/10.5281/zenodo.1405489.
We also encourage other researchers to share their datasets to facilitate
research on post-seismic geological hazards.
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