Árpád Berta

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Smart portable devices form the largest personal computing platform so far in human history, yet the adoption of P2P techniques has been very slow. One reason is the lack of a detailed understanding of the dynamic patterns of network connectivity and battery usage. For example, we know that when a smartphone is on a charger connected to a WiFi network(More)
Low-rank matrix approximation is an important tool in data mining with a wide range of applications, including recommender systems, clustering, and identifying topics in documents. When the matrix to be approximated originates from a large distributed system, such as a network of mobile phones or smart meters, a challenging problem arises due to the(More)
Stochastic gradient descent (SGD) is one of the most applied machine learning algorithms in unreliable large-scale decentralized environments. In this type of environment data privacy is a fundamental concern. The most popular way to investigate this topic is based on the framework of differential privacy. However, many important implementation details and(More)
One of the most fundamental data processing approach is the clustering. This is even true in distributed architectures. Here, we focus on the problem of designing efficient and fast K-Means approaches which work in fully distributed, asynchronous networks without any central control. We assume that the network has a huge number of computational units (even(More)
Decentralized learning algorithms are very sensitive to the size of the raw data records due to the resulting large communication cost. This can, in the worst case, even make decentralized learning infeasible. Dimension reduction is a key technique to compress data and to obtain small models. In this paper, we propose a number of robust and efficient(More)
Gossip learning is a form of decentralized stochastic gradient descent search that is implemented through randomized walks within a network. Our goal is to enable one to deploy gossip learning in open distributed systems, for example, in overlay networks formed by mobile devices, where different data mining tasks could be launched by many users. Among the(More)
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