With the advent of the internet of things and industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning models are trained to derive higher level knowledge. With the recent development of specialized machine learning hardware for mobile devices, a new era of distributed learning is about to begin that raises a new research question: How can we search in distributed machine learning models? Machine learning at the edge of the network has many benefits, such as low-latency inference and increased privacy. Such distributed machine learning models can also learn personalized for a human user, a specific context, or application scenario. As training data stays on the devices, control over possibly sensitive data is preserved as it is not shared with a third party. This new form of distributed learning leads to the partitioning of knowledge between many devices which makes access difficult. In this paper we tackle the problem of finding specific knowledge by forwarding a search request (query) to a device that can answer it best. To that end, we use a entropy based quality metric that takes the context of a query and the learning quality of a device into account. We show that our forwarding strategy can achieve over 95 accuracy in a urban mobility scenario where we use data from 30 000 people commuting in the city of Trento, Italy.
The second generation of P2P systems (e.g. Mercury @cite_21 , Squid @cite_3 , and Znet @cite_27 ) introduced the support for more complex, multidimensional, and range queries. This enabled searches like . These systems enable search in multidimensional space, where Data locality is usually achieved by dimension reduction techniques, such as space filling curves (e.g. @cite_19 ). A general problem is that range queries might be too restricted in cases with sparse data. For example, if there are very few results for the above mentioned query, results for persons slightly older than 20 years would also be interesting for the user.
@cite_19 @cite_27 @cite_21 @cite_3
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Today's peer-to-peer (P2P) systems are unable to cope well with range queries on multi-dimensional data. To extend existing P2P systems and thus support multidimensional range queries, one needs to consider such issues as space partitioning and mapping, efficient query processing, and load balancing. In this paper, the authors describe a scheme called ZNet, which addresses all these issues. Moreover, an extensive performance study which evaluates ZNet against several recent proposals was conducted, and results show that ZNet possesses nearly all desirable properties, while others typically fail in one or another. Conventional spatial queries, such as range search and nearest neighbor retrieval, involve only conditions on objects' geometric properties. Today, many modern applications call for novel forms of queries that aim to find objects satisfying both a spatial predicate, and a predicate on their associated texts. For example, instead of considering all the restaurants, a nearest neighbor query would instead ask for the restaurant that is the closest among those whose menus contain “steak, spaghetti, brandy” all at the same time. Currently, the best solution to such queries is based on the IR 2-tree, which, as shown in this paper, has a few deficiencies that seriously impact its efficiency. Motivated by this, we develop a new access method called the spatial inverted index that extends the conventional inverted index to cope with multidimensional data, and comes with algorithms that can answer nearest neighbor queries with keywords in real time. As verified by experiments, the proposed techniques outperform the IR 2-tree in query response time significantly, often by a factor of orders of magnitude. Peer-to-peer systems enable access to data spread over an extremely large number of machines. Most P2P systems support only simple lookup queries. However, many new applications, such as P2P photo sharing and massively multi-player games, would benefit greatly from support for multidimensional range queries. We show how such queries may be supported in a P2P system by adapting traditional spatial-database technologies with novel P2P routing networks and load-balancing algorithms. We show how to adapt two popular spatial-database solutions - kd-trees and space-filling curves - and experimentally compare their effectiveness. Given a textual description of an image, phrase grounding localizes objects in the image referred by query phrases in the description. State-of-the-art methods address the problem by ranking a set of proposals based on the relevance to each query, which are limited by the performance of independent proposal generation systems and ignore useful cues from context in the description. In this paper, we adopt a spatial regression method to break the performance limit, and introduce reinforcement learning techniques to further leverage semantic context information. We propose a novel Query-guided Regression network with Context policy (QRC Net) which jointly learns a Proposal Generation Network (PGN), a Query-guided Regression Network (QRN) and a Context Policy Network (CPN). Experiments show QRC Net provides a significant improvement in accuracy on two popular datasets: Flickr30K Entities and Referit Game, with 14.25 and 17.14 increase over the state-of-the-arts respectively.