1,164 research outputs found
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence
Smartphones, wearables, and Internet of Things (IoT) devices produce a wealth
of data that cannot be accumulated in a centralized repository for learning
supervised models due to privacy, bandwidth limitations, and the prohibitive
cost of annotations. Federated learning provides a compelling framework for
learning models from decentralized data, but conventionally, it assumes the
availability of labeled samples, whereas on-device data are generally either
unlabeled or cannot be annotated readily through user interaction. To address
these issues, we propose a self-supervised approach termed
\textit{scalogram-signal correspondence learning} based on wavelet transform to
learn useful representations from unlabeled sensor inputs, such as
electroencephalography, blood volume pulse, accelerometer, and WiFi channel
state information. Our auxiliary task requires a deep temporal neural network
to determine if a given pair of a signal and its complementary viewpoint (i.e.,
a scalogram generated with a wavelet transform) align with each other or not
through optimizing a contrastive objective. We extensively assess the quality
of learned features with our multi-view strategy on diverse public datasets,
achieving strong performance in all domains. We demonstrate the effectiveness
of representations learned from an unlabeled input collection on downstream
tasks with training a linear classifier over pretrained network, usefulness in
low-data regime, transfer learning, and cross-validation. Our methodology
achieves competitive performance with fully-supervised networks, and it
outperforms pre-training with autoencoders in both central and federated
contexts. Notably, it improves the generalization in a semi-supervised setting
as it reduces the volume of labeled data required through leveraging
self-supervised learning.Comment: Accepted for publication at IEEE Internet of Things Journa
Federated Transfer Learning with Multimodal Data
Smart cars, smartphones and other devices in the Internet of Things (IoT),
which usually have more than one sensors, produce multimodal data. Federated
Learning supports collecting a wealth of multimodal data from different devices
without sharing raw data. Transfer Learning methods help transfer knowledge
from some devices to others. Federated Transfer Learning methods benefit both
Federated Learning and Transfer Learning. This newly proposed Federated
Transfer Learning framework aims at connecting data islands with privacy
protection. Our construction is based on Federated Learning and Transfer
Learning. Compared with previous Federated Transfer Learnings, where each user
should have data with identical modalities (either all unimodal or all
multimodal), our new framework is more generic, it allows a hybrid distribution
of user data. The core strategy is to use two different but inherently
connected training methods for our two types of users. Supervised Learning is
adopted for users with only unimodal data (Type 1), while Self-Supervised
Learning is applied to user with multimodal data (Type 2) for both the feature
of each modality and the connection between them. This connection knowledge of
Type 2 will help Type 1 in later stages of training. Training in the new
framework can be divided in three steps. In the first step, users who have data
with the identical modalities are grouped together. For example, user with only
sound signals are in group one, and those with only images are in group two,
and users with multimodal data are in group three, and so on. In the second
step, Federated Learning is executed within the groups, where Supervised
Learning and Self-Supervised Learning are used depending on the group's nature.
Most of the Transfer Learning happens in the third step, where the related
parts in the network obtained from the previous steps are aggregated
(federated).Comment: 73 pages, 54 figures, master thesi
A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems
Anomaly detection is a crucial task in complex distributed systems. A
thorough understanding of the requirements and challenges of anomaly detection
is pivotal to the security of such systems, especially for real-world
deployment. While there are many works and application domains that deal with
this problem, few have attempted to provide an in-depth look at such systems.
In this survey, we explore the potentials of graph-based algorithms to identify
anomalies in distributed systems. These systems can be heterogeneous or
homogeneous, which can result in distinct requirements. One of our objectives
is to provide an in-depth look at graph-based approaches to conceptually
analyze their capability to handle real-world challenges such as heterogeneity
and dynamic structure. This study gives an overview of the State-of-the-Art
(SotA) research articles in the field and compare and contrast their
characteristics. To facilitate a more comprehensive understanding, we present
three systems with varying abstractions as use cases. We examine the specific
challenges involved in anomaly detection within such systems. Subsequently, we
elucidate the efficacy of graphs in such systems and explicate their
advantages. We then delve into the SotA methods and highlight their strength
and weaknesses, pointing out the areas for possible improvements and future
works.Comment: The first two authors (A. Danesh Pazho and G. Alinezhad Noghre) have
equal contribution. The article is accepted by IEEE Transactions on Knowledge
and Data Engineerin
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning
Federated learning (FL) enables a decentralized machine learning paradigm for
multiple clients to collaboratively train a generalized global model without
sharing their private data. Most existing works simply propose typical FL
systems for single-modal data, thus limiting its potential on exploiting
valuable multimodal data for future personalized applications. Furthermore, the
majority of FL approaches still rely on the labeled data at the client side,
which is limited in real-world applications due to the inability of
self-annotation from users. In light of these limitations, we propose a novel
multimodal FL framework that employs a semi-supervised learning approach to
leverage the representations from different modalities. Bringing this concept
into a system, we develop a distillation-based multimodal embedding knowledge
transfer mechanism, namely FedMEKT, which allows the server and clients to
exchange the joint knowledge of their learning models extracted from a small
multimodal proxy dataset. Our FedMEKT iteratively updates the generalized
global encoders with the joint embedding knowledge from the participating
clients. Thereby, to address the modality discrepancy and labeled data
constraint in existing FL systems, our proposed FedMEKT comprises local
multimodal autoencoder learning, generalized multimodal autoencoder
construction, and generalized classifier learning. Through extensive
experiments on three multimodal human activity recognition datasets, we
demonstrate that FedMEKT achieves superior global encoder performance on linear
evaluation and guarantees user privacy for personal data and model parameters
while demanding less communication cost than other baselines
Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey
Ubiquitous in-home health monitoring systems have become popular in recent
years due to the rise of digital health technologies and the growing demand for
remote health monitoring. These systems enable individuals to increase their
independence by allowing them to monitor their health from the home and by
allowing more control over their well-being. In this study, we perform a
comprehensive survey on this topic by reviewing a large number of literature in
the area. We investigate these systems from various aspects, namely sensing
technologies, communication technologies, intelligent and computing systems,
and application areas. Specifically, we provide an overview of in-home health
monitoring systems and identify their main components. We then present each
component and discuss its role within in-home health monitoring systems. In
addition, we provide an overview of the practical use of ubiquitous
technologies in the home for health monitoring. Finally, we identify the main
challenges and limitations based on the existing literature and provide eight
recommendations for potential future research directions toward the development
of in-home health monitoring systems. We conclude that despite extensive
research on various components needed for the development of effective in-home
health monitoring systems, the development of effective in-home health
monitoring systems still requires further investigation.Comment: 35 pages, 5 figure
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends
Recent advances in artificial intelligence (AI) have significantly
intensified research in the geoscience and remote sensing (RS) field. AI
algorithms, especially deep learning-based ones, have been developed and
applied widely to RS data analysis. The successful application of AI covers
almost all aspects of Earth observation (EO) missions, from low-level vision
tasks like super-resolution, denoising and inpainting, to high-level vision
tasks like scene classification, object detection and semantic segmentation.
While AI techniques enable researchers to observe and understand the Earth more
accurately, the vulnerability and uncertainty of AI models deserve further
attention, considering that many geoscience and RS tasks are highly
safety-critical. This paper reviews the current development of AI security in
the geoscience and RS field, covering the following five important aspects:
adversarial attack, backdoor attack, federated learning, uncertainty and
explainability. Moreover, the potential opportunities and trends are discussed
to provide insights for future research. To the best of the authors' knowledge,
this paper is the first attempt to provide a systematic review of AI
security-related research in the geoscience and RS community. Available code
and datasets are also listed in the paper to move this vibrant field of
research forward
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