Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …

Federated learning (FL) involves multiple distributed devices jointly training a shared model
without any of the participants having to reveal their local data to a centralized server. Most …

With the rapid development of information technology and instant messaging, many short
text data streams are continuously generated. Due to short texts' inherent features such as …

We implement a decentralized method for collaborative filtering via matrix factorization that
can rest on peer-to-peer Distributed Hash Table architectures like Pastry and Chord, for the …

Sensors and internet of things (IoTs) are ubiquitous in our modern day-to-day living. The
past decade has been marked by the rapid emergence and proliferation of a myriad of small …

Classification algorithms take their decisions according to a learning process on the training
set. Therefore, the data to be classified in the test set must have the same distribution as the …

Az adatbányászati és gépi tanuló algoritmusok jelen vannak a digitális életünkben, még ha
ez nem is tunik fel nekünk. Ezen algoritmusok szolgálnak a megtéveszto levelek kiszurésére …

The main goal of this thesis was to present a possible way of machine learning on fully
distributed data without collecting and storing it in a central location. Here, we expect that a …

This report reviews selected papers from the ones presented in the seminar on Network and
Cloud Analytics since April 2015 till November 2015 (realm. sics. se). During this time …