180 research outputs found
Offset quantum-well method for tunable distributed Bragg reflector lasers and electro-absorption modulated distributed feedback lasers
A two-section offset quantum-well structure tunable laser with a tuning range of 7 nm was fabricated using offset quantum-well method. The distributed Bragg reflector (DBR) was realized just by selectively wet etching the multiquantum-well (MQW) layer above the quaternary lower waveguide. A threshold current of 32 mA and an output power of 9 mW at 100 mA were achieved. Furthermore, with this offset structure method, a distributed feedback (DFB) laser was integrated with an electro-absorption modulator (EAM), which was capable of producing 20 dB of optical extinction
Towards Adversarially Robust Continual Learning
Recent studies show that models trained by continual learning can achieve the
comparable performances as the standard supervised learning and the learning
flexibility of continual learning models enables their wide applications in the
real world. Deep learning models, however, are shown to be vulnerable to
adversarial attacks. Though there are many studies on the model robustness in
the context of standard supervised learning, protecting continual learning from
adversarial attacks has not yet been investigated. To fill in this research
gap, we are the first to study adversarial robustness in continual learning and
propose a novel method called \textbf{T}ask-\textbf{A}ware \textbf{B}oundary
\textbf{A}ugmentation (TABA) to boost the robustness of continual learning
models. With extensive experiments on CIFAR-10 and CIFAR-100, we show the
efficacy of adversarial training and TABA in defending adversarial attacks.Comment: ICASSP 202
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
Home appliance manufacturers strive to obtain feedback from users to improve
their products and services to build a smart home system. To help manufacturers
develop a smart home system, we design a federated learning (FL) system
leveraging the reputation mechanism to assist home appliance manufacturers to
train a machine learning model based on customers' data. Then, manufacturers
can predict customers' requirements and consumption behaviors in the future.
The working flow of the system includes two stages: in the first stage,
customers train the initial model provided by the manufacturer using both the
mobile phone and the mobile edge computing (MEC) server. Customers collect data
from various home appliances using phones, and then they download and train the
initial model with their local data. After deriving local models, customers
sign on their models and send them to the blockchain. In case customers or
manufacturers are malicious, we use the blockchain to replace the centralized
aggregator in the traditional FL system. Since records on the blockchain are
untampered, malicious customers or manufacturers' activities are traceable. In
the second stage, manufacturers select customers or organizations as miners for
calculating the averaged model using received models from customers. By the end
of the crowdsourcing task, one of the miners, who is selected as the temporary
leader, uploads the model to the blockchain. To protect customers' privacy and
improve the test accuracy, we enforce differential privacy on the extracted
features and propose a new normalization technique. We experimentally
demonstrate that our normalization technique outperforms batch normalization
when features are under differential privacy protection. In addition, to
attract more customers to participate in the crowdsourcing FL task, we design
an incentive mechanism to award participants.Comment: This paper appears in IEEE Internet of Things Journal (IoT-J
Privacy and Robustness in Federated Learning: Attacks and Defenses
As data are increasingly being stored in different silos and societies
becoming more aware of data privacy issues, the traditional centralized
training of artificial intelligence (AI) models is facing efficiency and
privacy challenges. Recently, federated learning (FL) has emerged as an
alternative solution and continue to thrive in this new reality. Existing FL
protocol design has been shown to be vulnerable to adversaries within or
outside of the system, compromising data privacy and system robustness. Besides
training powerful global models, it is of paramount importance to design FL
systems that have privacy guarantees and are resistant to different types of
adversaries. In this paper, we conduct the first comprehensive survey on this
topic. Through a concise introduction to the concept of FL, and a unique
taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against
robustness; 3) inference attacks and defenses against privacy, we provide an
accessible review of this important topic. We highlight the intuitions, key
techniques as well as fundamental assumptions adopted by various attacks and
defenses. Finally, we discuss promising future research directions towards
robust and privacy-preserving federated learning.Comment: arXiv admin note: text overlap with arXiv:2003.02133; text overlap
with arXiv:1911.11815 by other author
Privacy-preserving Anomaly Detection in Cloud Manufacturing via Federated Transformer
With the rapid development of cloud manufacturing, industrial production with
edge computing as the core architecture has been greatly developed. However,
edge devices often suffer from abnormalities and failures in industrial
production. Therefore, detecting these abnormal situations timely and
accurately is crucial for cloud manufacturing. As such, a straightforward
solution is that the edge device uploads the data to the cloud for anomaly
detection. However, Industry 4.0 puts forward higher requirements for data
privacy and security so that it is unrealistic to upload data from edge devices
directly to the cloud. Considering the above-mentioned severe challenges, this
paper customizes a weakly-supervised edge computing anomaly detection
framework, i.e., Federated Learning-based Transformer framework
(\textit{FedAnomaly}), to deal with the anomaly detection problem in cloud
manufacturing. Specifically, we introduce federated learning (FL) framework
that allows edge devices to train an anomaly detection model in collaboration
with the cloud without compromising privacy. To boost the privacy performance
of the framework, we add differential privacy noise to the uploaded features.
To further improve the ability of edge devices to extract abnormal features, we
use the Transformer to extract the feature representation of abnormal data. In
this context, we design a novel collaborative learning protocol to promote
efficient collaboration between FL and Transformer. Furthermore, extensive case
studies on four benchmark data sets verify the effectiveness of the proposed
framework. To the best of our knowledge, this is the first time integrating FL
and Transformer to deal with anomaly detection problems in cloud manufacturing
Local Differential Privacy based Federated Learning for Internet of Things
Internet of Vehicles (IoV) is a promising branch of the Internet of Things.
IoV simulates a large variety of crowdsourcing applications such as Waze, Uber,
and Amazon Mechanical Turk, etc. Users of these applications report the
real-time traffic information to the cloud server which trains a machine
learning model based on traffic information reported by users for intelligent
traffic management. However, crowdsourcing application owners can easily infer
users' location information, which raises severe location privacy concerns of
the users. In addition, as the number of vehicles increases, the frequent
communication between vehicles and the cloud server incurs unexpected amount of
communication cost. To avoid the privacy threat and reduce the communication
cost, in this paper, we propose to integrate federated learning and local
differential privacy (LDP) to facilitate the crowdsourcing applications to
achieve the machine learning model. Specifically, we propose four LDP
mechanisms to perturb gradients generated by vehicles. The Three-Outputs
mechanism is proposed which introduces three different output possibilities to
deliver a high accuracy when the privacy budget is small. The output
possibilities of Three-Outputs can be encoded with two bits to reduce the
communication cost. Besides, to maximize the performance when the privacy
budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We
further propose a suboptimal mechanism (PM-SUB) with a simple formula and
comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by
combining Three-Outputs and PM-SUB.Comment: This paper appears in IEEE Internet of Things Journal (IoT-J
Field Implementation of Precision Livestock Farming: Selected Proceedings from the 2nd U.S. Precision Livestock Farming Conference
Precision Livestock Farming (PLF) involves the real-time monitoring of images, sounds, and other biological, physiological, and environmental parameters to assess and improve animal health and welfare within intensive and extensive production systems. Precision Livestock Farming systems have the potential to empower farmers to make better management decisions, based upon objective measures, during an animal production cycle. The potential benefits from PLF systems can only be realized if these systems are adopted in practice by livestock and poultry producers. The advancement of research in digital agriculture, and, more specifically, PLF technologies, over the past decade has been very impressive; however, the field implementation of PLF technology has not occurred at the same rate as research advancements.This proceeding is published as Zhao, Yang, Brett C. Ramirez, Janice M. Siegford, Hao Gan, Lingjuan Wang-Li, Daniel Berckmans, and Robert T. Burns. "Field Implementation of Precision Livestock Farming: Selected Proceedings from the 2nd US Precision Livestock Farming Conference." Animals 14, no. 7 (2024): 1128. doi: https://doi.org/10.3390/ani14071128. Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)
- …