855,003 research outputs found
Distributed One-class Learning
We propose a cloud-based filter trained to block third parties from uploading
privacy-sensitive images of others to online social media. The proposed filter
uses Distributed One-Class Learning, which decomposes the cloud-based filter
into multiple one-class classifiers. Each one-class classifier captures the
properties of a class of privacy-sensitive images with an autoencoder. The
multi-class filter is then reconstructed by combining the parameters of the
one-class autoencoders. The training takes place on edge devices (e.g.
smartphones) and therefore users do not need to upload their private and/or
sensitive images to the cloud. A major advantage of the proposed filter over
existing distributed learning approaches is that users cannot access, even
indirectly, the parameters of other users. Moreover, the filter can cope with
the imbalanced and complex distribution of the image content and the
independent probability of addition of new users. We evaluate the performance
of the proposed distributed filter using the exemplar task of blocking a user
from sharing privacy-sensitive images of other users. In particular, we
validate the behavior of the proposed multi-class filter with
non-privacy-sensitive images, the accuracy when the number of classes
increases, and the robustness to attacks when an adversary user has access to
privacy-sensitive images of other users
Learning from distributed data sources using random vector functional-link networks
One of the main characteristics in many real-world big data scenarios is their distributed nature. In a machine learning context, distributed data, together with the requirements of preserving privacy and scaling up to large networks, brings the challenge of designing fully decentralized training protocols. In this paper, we explore the problem of distributed learning when the features of every pattern are available throughout multiple agents (as is happening, for example, in a distributed database scenario). We propose an algorithm for a particular class of neural networks, known as Random Vector Functional-Link (RVFL), which is based on the Alternating Direction Method of Multipliers optimization algorithm. The proposed algorithm allows to learn an RVFL network from multiple distributed data sources, while restricting communication to the unique operation of computing a distributed average. Our experimental simulations show that the algorithm is able to achieve a generalization accuracy comparable to a fully centralized solution, while at the same time being extremely efficient
An Experimental Study of Class Imbalance in Federated Learning
Federated learning is a distributed machine learning paradigm that trains a
global model for prediction based on a number of local models at clients while
local data privacy is preserved. Class imbalance is believed to be one of the
factors that degrades the global model performance. However, there has been
very little research on if and how class imbalance can affect the global
performance. class imbalance in federated learning is much more complex than
that in traditional non-distributed machine learning, due to different class
imbalance situations at local clients. Class imbalance needs to be re-defined
in distributed learning environments. In this paper, first, we propose two new
metrics to define class imbalance -- the global class imbalance degree (MID)
and the local difference of class imbalance among clients (WCS). Then, we
conduct extensive experiments to analyze the impact of class imbalance on the
global performance in various scenarios based on our definition. Our results
show that a higher MID and a larger WCS degrade more the performance of the
global model. Besides, WCS is shown to slow down the convergence of the global
model by misdirecting the optimization
-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations
The paper considers a class of multi-agent Markov decision processes (MDPs),
in which the network agents respond differently (as manifested by the
instantaneous one-stage random costs) to a global controlled state and the
control actions of a remote controller. The paper investigates a distributed
reinforcement learning setup with no prior information on the global state
transition and local agent cost statistics. Specifically, with the agents'
objective consisting of minimizing a network-averaged infinite horizon
discounted cost, the paper proposes a distributed version of -learning,
-learning, in which the network agents collaborate by means of
local processing and mutual information exchange over a sparse (possibly
stochastic) communication network to achieve the network goal. Under the
assumption that each agent is only aware of its local online cost data and the
inter-agent communication network is \emph{weakly} connected, the proposed
distributed scheme is almost surely (a.s.) shown to yield asymptotically the
desired value function and the optimal stationary control policy at each
network agent. The analytical techniques developed in the paper to address the
mixed time-scale stochastic dynamics of the \emph{consensus + innovations}
form, which arise as a result of the proposed interactive distributed scheme,
are of independent interest.Comment: Submitted to the IEEE Transactions on Signal Processing, 33 page
PENGGUNAAN PENDEKATAN MATEMATIKA REALISTIK UNTUK MENINGKATKAN HASIL BELAJAR SISWA PADA PELAJARAN MATEMATIKA SISWA KELAS II SEKOLAH DASAR
The purpose of this study was to determine student learning outcomes in thematic mathematics learning before and after applying a realistic mathematical approach and to determine its improvement. The method to be used in this study is the Quasi Experiment method. The independent variable is learning mathematics using a realistic mathematical approach. While the dependent variable is student learning outcomes in mathematics. The type of data used is quantitative data in the form of test scores on student learning outcomes and qualitative results from observation of learning activities using realistic mathematical approaches. The population in this study were grade 2 elementary school students in Bandung in the academic year 2018/2019.Posttest results, obtained the significance value of the control class 0.001 (data not normally distributed) and experimental class 0.173 (data are normally distributed). Significance value For the average difference test / Maan Whitney is 0.731, the posttest score of the experimental class students is significantly better than the control class. Significance value N gain 0.002 control class and the significance value of the experimental class 0.047 means that the two classes are not normally distributed. Obtained a significance value of 0.634, because a one-party test is obtained, 0.634 / 2 = 0.317. This value is> 0.05 so H0 is rejected. So, increasing the ability to understand concepts in class 2 with learning using Realistic Mathematics is better than ordinary learning
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