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.
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. 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. This paper presents the design of Mercury, a scalable protocol for supporting multi-attribute range-based searches. Mercury differs from previous range-based query systems in that it supports multiple attributes as well as performs explicit load balancing. To guarantee efficient routing and load balancing, Mercury uses novel light-weight sampling mechanisms for uniformly sampling random nodes in a highly dynamic overlay network. Our evaluation shows that Mercury is able to achieve its goals of logarithmic-hop routing and near-uniform load balancing.We also show that Mercury can be used to solve a key problem for an important class of distributed applications: distributed state maintenance for distributed games. We show that the Mercury-based solution is easy to use, and that it reduces the game's messaging overheard significantly compared to a naive approach. A fundamental problem in large scale, decentralized distributed systems is the efficient discovery of information. This paper presents Squid, a peer-to-peer information discovery system that supports flexible searches and provides search guarantees. The fundamental concept underlying the approach is the definition of multi-dimensional information spaces and the maintenance of locality in these spaces. The key innovation is a dimensionality reducing indexing scheme that effectively maps the multi-dimensional information space to physical peers while preserving lexical locality. Squid supports complex queries containing partial keywords, wildcards and ranges. Analytical and simulation results show that Squid is scalable and efficient.