3 research outputs found
Detection of Insider Attacks in Distributed Projected Subgradient Algorithms
The gossip-based distributed algorithms are widely used to solve
decentralized optimization problems in various multi-agent applications, while
they are generally vulnerable to data injection attacks by internal malicious
agents as each agent locally estimates its decent direction without an
authorized supervision. In this work, we explore the application of artificial
intelligence (AI) technologies to detect internal attacks. We show that a
general neural network is particularly suitable for detecting and localizing
the malicious agents, as they can effectively explore nonlinear relationship
underlying the collected data. Moreover, we propose to adopt one of the
state-of-art approaches in federated learning, i.e., a collaborative
peer-to-peer machine learning protocol, to facilitate training our neural
network models by gossip exchanges. This advanced approach is expected to make
our model more robust to challenges with insufficient training data, or
mismatched test data. In our simulations, a least-squared problem is considered
to verify the feasibility and effectiveness of AI-based methods. Simulation
results demonstrate that the proposed AI-based methods are beneficial to
improve performance of detecting and localizing malicious agents over
score-based methods, and the peer-to-peer neural network model is indeed robust
to target issues
Consensus Based Multi-Layer Perceptrons for Edge Computing
In recent years, storing large volumes of data on distributed devices has
become commonplace. Applications involving sensors, for example, capture data
in different modalities including image, video, audio, GPS and others. Novel
algorithms are required to learn from this rich distributed data. In this
paper, we present consensus based multi-layer perceptrons for
resource-constrained devices. Assuming nodes (devices) in the distributed
system are arranged in a graph and contain vertically partitioned data, the
goal is to learn a global function that minimizes the loss. Each node learns a
feed-forward multi-layer perceptron and obtains a loss on data stored locally.
It then gossips with a neighbor, chosen uniformly at random, and exchanges
information about the loss. The updated loss is used to run a back propagation
algorithm and adjust weights appropriately. This method enables nodes to learn
the global function without exchange of data in the network. Empirical results
reveal that the consensus algorithm converges to the centralized model and has
performance comparable to centralized multi-layer perceptrons and tree-based
algorithms including random forests and gradient boosted decision trees
Communication-Efficient Edge AI: Algorithms and Systems
Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide
range of fields, ranging from speech processing, image classification to drug
discovery. This is driven by the explosive growth of data, advances in machine
learning (especially deep learning), and easy access to vastly powerful
computing resources. Particularly, the wide scale deployment of edge devices
(e.g., IoT devices) generates an unprecedented scale of data, which provides
the opportunity to derive accurate models and develop various intelligent
applications at the network edge. However, such enormous data cannot all be
sent from end devices to the cloud for processing, due to the varying channel
quality, traffic congestion and/or privacy concerns. By pushing inference and
training processes of AI models to edge nodes, edge AI has emerged as a
promising alternative. AI at the edge requires close cooperation among edge
devices, such as smart phones and smart vehicles, and edge servers at the
wireless access points and base stations, which however result in heavy
communication overheads. In this paper, we present a comprehensive survey of
the recent developments in various techniques for overcoming these
communication challenges. Specifically, we first identify key communication
challenges in edge AI systems. We then introduce communication-efficient
techniques, from both algorithmic and system perspectives for training and
inference tasks at the network edge. Potential future research directions are
also highlighted.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl