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M.: Asynchronous peer-to-peer data mining with stochastic gradient descent
- In: Euro-Par 2011. LNCS
"... Abstract. Fully distributed data mining algorithms build global models over large amounts of data distributed over a large number of peers in a network, without moving the data itself. In the area of peer-to-peer (P2P) networks, such algorithms have various applications in P2P social networking, and ..."
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Abstract. Fully distributed data mining algorithms build global models over large amounts of data distributed over a large number of peers in a network, without moving the data itself. In the area of peer-to-peer (P2P) networks, such algorithms have various applications in P2P social networking, and also in trackerless BitTorrent communities. The difficulty of the problem involves realizing good quality models with an affordable communication complexity, while assuming as little as possible about the communication model. Here we describe a conceptually simple, yet powerful generic approach for designing efficient, fully distributed, asynchronous, local algorithms for learning models of fully distributed data. The key idea is that many models perform a random walk over the network while being gradually adjusted to fit the data they encounter, using a stochastic gradient descent search. We demonstrate our approach by implementing the support vector machine (SVM) method and by experimentally evaluating its performance in various failure scenarios over different benchmark datasets. Our algorithm scheme can implement a wide range of machine learning methods in an extremely robust manner. 1
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, 2015
"... HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte p ..."
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HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Asynchronous decentralized convex optimization through short-term gradient averaging
Detecting Concept Drift in Fully Distributed Environments
"... Abstract—Applying sophisticated machine learning tech-niques on fully distributed data is increasingly important in many applications like distributed recommender systems or spam filters. In this type of networked environment the data model can change dynamically over time (concept drift). Iden-tify ..."
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Abstract—Applying sophisticated machine learning tech-niques on fully distributed data is increasingly important in many applications like distributed recommender systems or spam filters. In this type of networked environment the data model can change dynamically over time (concept drift). Iden-tifying when concept drift occurred is a key for several drift handling techniques and important in numerous scenarios. However drift handling approaches exist, no efficient solution for detecting the drift is known in very large scale networks. Here, we propose an approach that can detect the concept drift in large scale and fully distributed networks. In our approach, the learning is performed by applying online learners that take random walks in the network while updating themselves using the samples available at the nodes. The drift detection is based on an adaptive mechanism which uses the historical performances of the models. Through empirical evaluations we demonstrate that our approach handles the drifting concept while additionally detects the occurrence of the concept drift with high accuracy. Keywords-adaptive classification; concept drift; P2P I.
