7,881 research outputs found
a combined top-down and bottom-up approach
The thesis focuses on the interoperability of autonomous legacy databases with
the idea of meeting the actual requirements of an organization. The
interoperability is resolved by combining the topdown and bottom-up
strategies. The legacy objects are extracted from the existing databases
through a database reverse engineering process. The business objects are
defined by both the organization requirements and the integration of the
legacy objects
Federated Computing for the Masses – Aggregating Resources to Tackle Large-scale Engineering Problems
The complexity of many problems in science and engineering requires computational capacity exceeding what average user can expect from a single computational center. While many of these problems can be viewed as a set of independent tasks, their collective complexity easily requires millions core-hours on any state-of-the-art HPC resource, and throughput that cannot be sustained by a single multi-user queuing system. In this paper we explore the use of aggregated HPC resources to solve large-scale engineering problems. We show it is possible to build a computational federation that is easy to use by end-users, and is elastic, resilient and scalable. We argue that the fusion of federated computing and real-life engineering problems can be brought to average user if relevant middleware is provided. We report on the use of federation of 10 distributed heterogeneous HPC resources to perform a large-scale interrogation of the parameter space in the microscale fluid flow problem
Requirements for IT Governance in Organizations Experiencing Decentralization
International audienceDecentralization of organizations and subsequent change of their management and operation styles require changes in organization's processes and heavily involve IT. Enterprise Architecture (EA) frameworks fit to primarily centralized organizational structures, and as such have shortcomings when used in decentralized organizations. We illustrate this idea on the example of one organization in the Higher Education sector that faces decentralization of its structure and has to adapt to it. Overcoming these challenges requires some new principles to be introduced and incorporated into the EA knowledge. In particular for IT governance, in this study we argue that peer-to-peer principles can offer more suitable governance over current EA frameworks as they are able to better align with decentralized components of an organizational structure
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Deep Learning has recently become hugely popular in machine learning,
providing significant improvements in classification accuracy in the presence
of highly-structured and large databases.
Researchers have also considered privacy implications of deep learning.
Models are typically trained in a centralized manner with all the data being
processed by the same training algorithm. If the data is a collection of users'
private data, including habits, personal pictures, geographical positions,
interests, and more, the centralized server will have access to sensitive
information that could potentially be mishandled. To tackle this problem,
collaborative deep learning models have recently been proposed where parties
locally train their deep learning structures and only share a subset of the
parameters in the attempt to keep their respective training sets private.
Parameters can also be obfuscated via differential privacy (DP) to make
information extraction even more challenging, as proposed by Shokri and
Shmatikov at CCS'15.
Unfortunately, we show that any privacy-preserving collaborative deep
learning is susceptible to a powerful attack that we devise in this paper. In
particular, we show that a distributed, federated, or decentralized deep
learning approach is fundamentally broken and does not protect the training
sets of honest participants. The attack we developed exploits the real-time
nature of the learning process that allows the adversary to train a Generative
Adversarial Network (GAN) that generates prototypical samples of the targeted
training set that was meant to be private (the samples generated by the GAN are
intended to come from the same distribution as the training data).
Interestingly, we show that record-level DP applied to the shared parameters of
the model, as suggested in previous work, is ineffective (i.e., record-level DP
is not designed to address our attack).Comment: ACM CCS'17, 16 pages, 18 figure
Federated Learning Hyper-Parameter Tuning from a System Perspective
Federated learning (FL) is a distributed model training paradigm that
preserves clients' data privacy. It has gained tremendous attention from both
academia and industry. FL hyper-parameters (e.g., the number of selected
clients and the number of training passes) significantly affect the training
overhead in terms of computation time, transmission time, computation load, and
transmission load. However, the current practice of manually selecting FL
hyper-parameters imposes a heavy burden on FL practitioners because
applications have different training preferences. In this paper, we propose
FedTune, an automatic FL hyper-parameter tuning algorithm tailored to
applications' diverse system requirements in FL training. FedTune iteratively
adjusts FL hyper-parameters during FL training and can be easily integrated
into existing FL systems. Through extensive evaluations of FedTune for diverse
applications and FL aggregation algorithms, we show that FedTune is lightweight
and effective, achieving 8.48%-26.75% system overhead reduction compared to
using fixed FL hyper-parameters. This paper assists FL practitioners in
designing high-performance FL training solutions. The source code of FedTune is
available at https://github.com/DataSysTech/FedTune.Comment: arXiv admin note: substantial text overlap with arXiv:2110.0306
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