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

Bioactive constituents and shelf-life of sweet potato (Ipomoea batatas L.) leaves

We aimed to evaluate the green biomass’ of sweet potato (Ipomoea batatas L.) quality, through quantitative analysis of microelements, colour characteristics, and UHPLC-MS screening of bioactive constituents. The shelf life examination included sealed raw sweet potato leaves in plastic packs were stored at 6°C and 12°C and the microbiological characteristics were monitored for 2 weeks, through enumeration of mesophilic total plate count, total fungi count, Enterobacteriaceae and mesophilic aerobic spores. We found, that the sweet potato leaves can be considered as the source of calcium, magnesium and phosphorus among the minerals, of which calcium is the most abundant. We identified 17 types of amino acids, 7 vitamins, mainly vitamins belonging to the Vitamin B family. Furthermore, it contained carboxylic acids, flavonoids, polyphenols and aromatic compounds. The sweet potato leaves stored at 6°C was of satisfactory microbiological quality on day 14. Our data suggest that the sweet potato leaves could be a valuable source for healthy nutrition