2,350 research outputs found
Distributed and Deep Vertical Federated Learning with Big Data
In recent years, data are typically distributed in multiple organizations
while the data security is becoming increasingly important. Federated Learning
(FL), which enables multiple parties to collaboratively train a model without
exchanging the raw data, has attracted more and more attention. Based on the
distribution of data, FL can be realized in three scenarios, i.e., horizontal,
vertical, and hybrid. In this paper, we propose to combine distributed machine
learning techniques with Vertical FL and propose a Distributed Vertical
Federated Learning (DVFL) approach. The DVFL approach exploits a fully
distributed architecture within each party in order to accelerate the training
process. In addition, we exploit Homomorphic Encryption (HE) to protect the
data against honest-but-curious participants. We conduct extensive
experimentation in a large-scale cluster environment and a cloud environment in
order to show the efficiency and scalability of our proposed approach. The
experiments demonstrate the good scalability of our approach and the
significant efficiency advantage (up to 6.8 times with a single server and 15.1
times with multiple servers in terms of the training time) compared with
baseline frameworks.Comment: To appear in CCPE (Concurrency and Computation: Practice and
Experience
Understanding Compressive Adversarial Privacy
Designing a data sharing mechanism without sacrificing too much privacy can
be considered as a game between data holders and malicious attackers. This
paper describes a compressive adversarial privacy framework that captures the
trade-off between the data privacy and utility. We characterize the optimal
data releasing mechanism through convex optimization when assuming that both
the data holder and attacker can only modify the data using linear
transformations. We then build a more realistic data releasing mechanism that
can rely on a nonlinear compression model while the attacker uses a neural
network. We demonstrate in a series of empirical applications that this
framework, consisting of compressive adversarial privacy, can preserve
sensitive information
Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations
In the growing world of artificial intelligence, federated learning is a
distributed learning framework enhanced to preserve the privacy of individuals'
data. Federated learning lays the groundwork for collaborative research in
areas where the data is sensitive. Federated learning has several implications
for real-world problems. In times of crisis, when real-time decision-making is
critical, federated learning allows multiple entities to work collectively
without sharing sensitive data. This distributed approach enables us to
leverage information from multiple sources and gain more diverse insights. This
paper is a systematic review of the literature on privacy-preserving machine
learning in the last few years based on the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, we have
presented an extensive review of supervised/unsupervised machine learning
algorithms, ensemble methods, meta-heuristic approaches, blockchain technology,
and reinforcement learning used in the framework of federated learning, in
addition to an overview of federated learning applications. This paper reviews
the literature on the components of federated learning and its applications in
the last few years. The main purpose of this work is to provide researchers and
practitioners with a comprehensive overview of federated learning from the
machine learning point of view. A discussion of some open problems and future
research directions in federated learning is also provided
Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference
Executing deep neural networks for inference on the server-class or cloud
backend based on data generated at the edge of Internet of Things is desirable
due primarily to the limited compute power of edge devices and the need to
protect the confidentiality of the inference neural networks. However, such a
remote inference scheme incurs concerns regarding the privacy of the inference
data transmitted by the edge devices to the curious backend. This paper
presents a lightweight and unobtrusive approach to obfuscate the inference data
at the edge devices. It is lightweight in that the edge device only needs to
execute a small-scale neural network; it is unobtrusive in that the edge device
does not need to indicate whether obfuscation is applied. Extensive evaluation
by three case studies of free spoken digit recognition, handwritten digit
recognition, and American sign language recognition shows that our approach
effectively protects the confidentiality of the raw forms of the inference data
while effectively preserving the backend's inference accuracy.Comment: This paper has been accepted by IEEE Internet of Things Journal,
Special Issue on Artificial Intelligence Powered Edge Computing for Internet
of Thing
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