109,274 research outputs found
Data Poisoning Attacks on Federated Machine Learning
Federated machine learning which enables resource constrained node devices
(e.g., mobile phones and IoT devices) to learn a shared model while keeping the
training data local, can provide privacy, security and economic benefits by
designing an effective communication protocol. However, the communication
protocol amongst different nodes could be exploited by attackers to launch data
poisoning attacks, which has been demonstrated as a big threat to most machine
learning models. In this paper, we attempt to explore the vulnerability of
federated machine learning. More specifically, we focus on attacking a
federated multi-task learning framework, which is a federated learning
framework via adopting a general multi-task learning framework to handle
statistical challenges. We formulate the problem of computing optimal poisoning
attacks on federated multi-task learning as a bilevel program that is adaptive
to arbitrary choice of target nodes and source attacking nodes. Then we propose
a novel systems-aware optimization method, ATTack on Federated Learning
(AT2FL), which is efficiency to derive the implicit gradients for poisoned
data, and further compute optimal attack strategies in the federated machine
learning. Our work is an earlier study that considers issues of data poisoning
attack for federated learning. To the end, experimental results on real-world
datasets show that federated multi-task learning model is very sensitive to
poisoning attacks, when the attackers either directly poison the target nodes
or indirectly poison the related nodes by exploiting the communication
protocol.Comment: 8pages,16 figure
Autonomous Wireless Systems with Artificial Intelligence
This paper discusses technology and opportunities to embrace artificial
intelligence (AI) in the design of autonomous wireless systems. We aim to
provide readers with motivation and general AI methodology of autonomous agents
in the context of self-organization in real time by unifying knowledge
management with sensing, reasoning and active learning. We highlight
differences between training-based methods for matching problems and
training-free methods for environment-specific problems. Finally, we
conceptually introduce the functions of an autonomous agent with knowledge
management
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
With the breakthroughs in deep learning, the recent years have witnessed a
booming of artificial intelligence (AI) applications and services, spanning
from personal assistant to recommendation systems to video/audio surveillance.
More recently, with the proliferation of mobile computing and
Internet-of-Things (IoT), billions of mobile and IoT devices are connected to
the Internet, generating zillions Bytes of data at the network edge. Driving by
this trend, there is an urgent need to push the AI frontiers to the network
edge so as to fully unleash the potential of the edge big data. To meet this
demand, edge computing, an emerging paradigm that pushes computing tasks and
services from the network core to the network edge, has been widely recognized
as a promising solution. The resulted new inter-discipline, edge AI or edge
intelligence, is beginning to receive a tremendous amount of interest. However,
research on edge intelligence is still in its infancy stage, and a dedicated
venue for exchanging the recent advances of edge intelligence is highly desired
by both the computer system and artificial intelligence communities. To this
end, we conduct a comprehensive survey of the recent research efforts on edge
intelligence. Specifically, we first review the background and motivation for
artificial intelligence running at the network edge. We then provide an
overview of the overarching architectures, frameworks and emerging key
technologies for deep learning model towards training/inference at the network
edge. Finally, we discuss future research opportunities on edge intelligence.
We believe that this survey will elicit escalating attentions, stimulate
fruitful discussions and inspire further research ideas on edge intelligence.Comment: Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang,
"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge
Computing," Proceedings of the IEE
Differentially Private Collaborative Intrusion Detection Systems For VANETs
Vehicular ad hoc network (VANET) is an enabling technology in modern
transportation systems for providing safety and valuable information, and yet
vulnerable to a number of attacks from passive eavesdropping to active
interfering. Intrusion detection systems (IDSs) are important devices that can
mitigate the threats by detecting malicious behaviors. Furthermore, the
collaborations among vehicles in VANETs can improve the detection accuracy by
communicating their experiences between nodes. To this end, distributed machine
learning is a suitable framework for the design of scalable and implementable
collaborative detection algorithms over VANETs. One fundamental barrier to
collaborative learning is the privacy concern as nodes exchange data among
them. A malicious node can obtain sensitive information of other nodes by
inferring from the observed data. In this paper, we propose a
privacy-preserving machine-learning based collaborative IDS (PML-CIDS) for
VANETs. The proposed algorithm employs the alternating direction method of
multipliers (ADMM) to a class of empirical risk minimization (ERM) problems and
trains a classifier to detect the intrusions in the VANETs. We use the
differential privacy to capture the privacy notation of the PML-CIDS and
propose a method of dual variable perturbation to provide dynamic differential
privacy. We analyze theoretical performance and characterize the fundamental
tradeoff between the security and privacy of the PML-CIDS. We also conduct
numerical experiments using the NSL-KDD dataset to corroborate the results on
the detection accuracy, security-privacy tradeoffs, and design
AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization
We study distributed stochastic convex optimization under the delayed
gradient model where the server nodes perform parameter updates, while the
worker nodes compute stochastic gradients. We discuss, analyze, and experiment
with a setup motivated by the behavior of real-world distributed computation
networks, where the machines are differently slow at different time. Therefore,
we allow the parameter updates to be sensitive to the actual delays
experienced, rather than to worst-case bounds on the maximum delay. This
sensitivity leads to larger stepsizes, that can help gain rapid initial
convergence without having to wait too long for slower machines, while
maintaining the same asymptotic complexity. We obtain encouraging improvements
to overall convergence for distributed experiments on real datasets with up to
billions of examples and features.Comment: 19 page
Reading Between the Pixels: Photographic Steganography for Camera Display Messaging
We exploit human color metamers to send light-modulated messages less visible
to the human eye, but recoverable by cameras. These messages are a key
component to camera-display messaging, such as handheld smartphones capturing
information from electronic signage. Each color pixel in the display image is
modified by a particular color gradient vector. The challenge is to find the
color gradient that maximizes camera response, while minimizing human response.
The mismatch in human spectral and camera sensitivity curves creates an
opportunity for hidden messaging. Our approach does not require knowledge of
these sensitivity curves, instead we employ a data-driven method. We learn an
ellipsoidal partitioning of the six-dimensional space of colors and color
gradients. This partitioning creates metamer sets defined by the base color at
the display pixel and the color gradient direction for message encoding. We
sample from the resulting metamer sets to find color steps for each base color
to embed a binary message into an arbitrary image with reduced visible
artifacts. Unlike previous methods that rely on visually obtrusive intensity
modulation, we embed with color so that the message is more hidden. Ordinary
displays and cameras are used without the need for expensive LEDs or high speed
devices. The primary contribution of this work is a framework to map the pixels
in an arbitrary image to a metamer pair for steganographic photo messaging.Comment: 16 pages with references 8 tables and figure
A Delay Optimal MAC and Packet Scheduler for Heterogeneous M2M Uplink
The uplink data arriving at the Machine-to-Machine (M2M) Application Server
(AS) via M2M Aggregators (MAs) is fairly heterogeneous along several dimensions
such as maximum tolerable packet delay, payload size and arrival rate, thus
necessitating the design of Quality-of-Service (QoS) aware packet scheduler. In
this paper, we classify the M2M uplink data into multiple QoS classes and use
sigmoidal function to map the delay requirements of each class onto utility
functions. We propose a proportionally fair delay-optimal multiclass packet
scheduler at AS that maximizes a system utility metric. We note that the
average class delay under any work-conserving scheduling policy can be realized
by appropriately time-sharing between all possible preemptive priority
policies. Therefore the optimal scheduler is determined using an iterative
process to determine the optimal time-sharing between all priority scheduling
policies, such that it results in maximum system utility. The proposed
scheduler can be implemented online with reduced complexity due to the
iterative optimization process. We then extend this work to determine jointly
optimal MA-AS channel allocation and packet scheduling scheme at the MAs and
AS. We first formulate a joint optimization problem that is solved centrally at
the AS and then propose a low complexity distributed optimization problem
solved independently at MAs and AS. We show that the distributed optimization
solution converges quickly to the centralized optimization result with minimal
information exchange overhead between MAs and AS. Using Monte-Carlo
simulations, we verify the optimality of the proposed scheduler and show that
it outperforms other state-of-the-art packet schedulers such as weighted round
robin, max-weight scheduler etc. Another desirable feature of proposed
scheduler is low delay jitter for delay-sensitive traffic.Comment: 35 pages, 14 figures, This paper is in part submitted to IEEE
Internet of Things Journa
Distributed Charging Control of Electric Vehicles Using Online Learning
We propose an algorithm for distributed charging control of electric vehicles
(EVs) using online learning and online convex optimization. Many distributed
charging control algorithms in the literature implicitly assume fast two-way
communication between a distribution company and EV customers. This assumption
is impractical at present and raises privacy and security concerns. Our
algorithm does not use this assumption; however, at the expense of slower
convergence to the optimal solution. The proposed algorithm requires one-way
communication, which is implemented through the distribution company publishing
the pricing profiles of the previous days. We provide convergence results of
the algorithm and illustrate the results through numerical examples.Comment: Submitted to IEEE Transactions on Automatic Contro
Transcribing Against Time
We investigate the problem of manually correcting errors from an automatic
speech transcript in a cost-sensitive fashion. This is done by specifying a
fixed time budget, and then automatically choosing location and size of
segments for correction such that the number of corrected errors is maximized.
The core components, as suggested by previous research [1], are a utility model
that estimates the number of errors in a particular segment, and a cost model
that estimates annotation effort for the segment. In this work we propose a
dynamic updating framework that allows for the training of cost models during
the ongoing transcription process. This removes the need for transcriber
enrollment prior to the actual transcription, and improves correction
efficiency by allowing highly transcriber-adaptive cost modeling. We first
confirm and analyze the improvements afforded by this method in a simulated
study. We then conduct a realistic user study, observing efficiency
improvements of 15% relative on average, and 42% for the participants who
deviated most strongly from our initial, transcriber-agnostic cost model.
Moreover, we find that our updating framework can capture dynamically changing
factors, such as transcriber fatigue and topic familiarity, which we observe to
have a large influence on the transcriber's working behavior.Comment: Speech Communication, Oct 2017 (preprint
Distributed Autonomous Online Learning: Regrets and Intrinsic Privacy-Preserving Properties
Online learning has become increasingly popular on handling massive data. The
sequential nature of online learning, however, requires a centralized learner
to store data and update parameters. In this paper, we consider online learning
with {\em distributed} data sources. The autonomous learners update local
parameters based on local data sources and periodically exchange information
with a small subset of neighbors in a communication network. We derive the
regret bound for strongly convex functions that generalizes the work by Ram et
al. (2010) for convex functions. Most importantly, we show that our algorithm
has \emph{intrinsic} privacy-preserving properties, and we prove the sufficient
and necessary conditions for privacy preservation in the network. These
conditions imply that for networks with greater-than-one connectivity, a
malicious learner cannot reconstruct the subgradients (and sensitive raw data)
of other learners, which makes our algorithm appealing in privacy sensitive
applications.Comment: 25 pages, 2 figure
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