T-MAN: gossip-based overlay topology management

Syftet med specialarbetet är att presentera genren allåldersböcker samt att ge litteraturtips till den intresserade läsaren. Med en kortfattad definition innebär begreppet allåldersböcker böcker som kan läsas med lika stor behållning av såväl barn och ungdom som vuxna läsare. Specialarbetet inleds med utdrag ur olika intervjuer som jag gjort med fackmänniskor i bokvärlden. Sedan följer ett fyrtiotal annotationer som jag skrivit efter att ha läst dessa allåldersböcker. Bokurvalet har gjorts efter rekommendationer av ovannämnda personer. Slutligen följer en förteckning över icke-annoterad allålderslitteratur som valts ut enligt samma principer som de övriga verken

Differentially Private Linear Models for Gossip Learning through Data Perturbation

Privacy is a key concern in many distributed systems that are rich in personal data such as networks of smart meters or smartphones. Decentralizing the processing of personal data in such systems is a promising first step towards achieving privacy through avoiding the collection of data altogether. However, decentralization in itself is not enough: Additional guarantees such as differential privacy are highly desirable. Here, we focus on stochastic gradient descent (SGD), a popular approach to implement distributed learning. Our goal is to design differentially private variants of SGD to be applied in gossip learning, a decentralized learning framework. Known approaches that are suitable for our scenario focus on protecting the gradient that is being computed in each iteration of SGD. This has the drawback that each data point can be accessed only a small number of times. We propose a solution in which we effectively publish the entire database in a differentially private way so that linear learners could be run that are allowed to access any (perturbed) data point any number of times. This flexibility is very useful when using the method in combination with distributed learning environments. We show empirically that the performance of the obtained model is comparable to that of previous gradient-based approaches and it is even superior in certain scenarios

Ordered slicing of very large-scale overlay networks

Climate change is expected to have a large impact on northern ecosystems. Increased temperatures and altered precipitation and snow cover patterns will have a great impact on subarctic tundra. Bryophytes form an important component of tundra ecosystems because of their high abundance and their importance in many ecological processes. The effect of elevation and snow cover on freezing damage in shoots of three subarctic bryophytes: Ptilidium ciliare, Hylocomium splendens and Sphagnum fuscum, was studied in a snow manipulation field experiment at different elevations in Abisko, Sweden, during early spring. The treatments included snow addition, snow removal and control. In addition, bryophyte healthiness at the plot scale was determined by image analysis using colour selection, and soil temperature and moisture data were collected. Freezing damage differed significantly among bryophyte species with P. ciliare having the lowest freezing damage. There was a decrease in freezing damage over time due to the increase in temperature as spring progressed. Counter expectation, freezing damage was higher at low elevation although the mean daily minimum temperature was lower at higher elevation, which might be due to adaptation effects. Snow treatment had only a minor effect on freezing damage, but it did have an effect on proportion of undamaged tissue at the plot scale which increased with increasing snow cover at high elevation, but decreased with increasing snow cover at low elevation. Soil moisture content was also affected by snow treatment. The number of freeze-thaw cycles was less for S. fuscum and H. splendens compared to bare soil plots, which indicates insulating capacities of these bryophytes. Freezing damage could not be explained by the measured climate variables alone; therefore, it is likely the result of a complex set of factors, possibly including solar radiation and disturbance by herbivores

Small degree BitTorrent

It is well-known that the BitTorrent file sharing protocol is responsible for a significant portion of the Internet traffic. A large amount of work has been devoted to reducing the footprint of the protocol in terms of the amount of traffic, however, its flow level footprint has not been studied in depth. We argue in this paper that the large amount of flows that a BitTorrent client maintains will not scale over a certain point. To solve this problem, we first examine the flow structure through realistic simulations. We find that only a few TCP connections are used frequently for data transfer, while most of the connections are used mostly for signaling. This makes it possible to separate the data and signaling paths. We propose that, as the signaling traffic provides little overhead, it should be transferred on a separate dedicated small degree overlay while the data traffic should utilize temporal TCP sockets active only during the data transfer. Through simulation we show that this separation has no significant effect on the performance of the BitTorrent protocol while we can drastically reduce the number of actual flows

Gossip Learning with Linear Models on Fully Distributed Data

Machine learning over fully distributed data poses an important problem in peer-to-peer (P2P) applications. In this model we have one data record at each network node, but without the possibility to move raw data due to privacy considerations. For example, user profiles, ratings, history, or sensor readings can represent this case. This problem is difficult, because there is no possibility to learn local models, the system model offers almost no guarantees for reliability, yet the communication cost needs to be kept low. Here we propose gossip learning, a generic approach that is based on multiple models taking random walks over the network in parallel, while applying an online learning algorithm to improve themselves, and getting combined via ensemble learning methods. We present an instantiation of this approach for the case of classification with linear models. Our main contribution is an ensemble learning method which---through the continuous combination of the models in the network---implements a virtual weighted voting mechanism over an exponential number of models at practically no extra cost as compared to independent random walks. We prove the convergence of the method theoretically, and perform extensive experiments on benchmark datasets. Our experimental analysis demonstrates the performance and robustness of the proposed approach.Comment: The paper was published in the journal Concurrency and Computation: Practice and Experience http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291532-0634 (DOI: http://dx.doi.org/10.1002/cpe.2858). The modifications are based on the suggestions from the reviewer