26 research outputs found
Linear Regression over Networks with Communication Guarantees
A key functionality of emerging connected autonomous systems such as smart
cities, smart transportation systems, and the industrial Internet-of-Things, is
the ability to process and learn from data collected at different physical
locations. This is increasingly attracting attention under the terms of
distributed learning and federated learning. However, in connected autonomous
systems, data transfer takes place over communication networks with often
limited resources. This paper examines algorithms for communication-efficient
learning for linear regression tasks by exploiting the informativeness of the
data. The developed algorithms enable a tradeoff between communication and
learning with theoretical performance guarantees and efficient practical
implementations.Comment: Accepted at 3rd Annual Learning for Dynamics & Control Conference
(L4DC) 2021. arXiv admin note: substantial text overlap with arXiv:2101.1000
Resource-Aware Design Of Wireless Control Systems
This work is motivated by modern monitoring and control infrastructures appearing in smart homes, urban environments, and industrial plants. These systems are characterized by multiple sensor and actuator devices at different physical locations, communicating wirelessly with each other. Desired monitoring and control performance requires efficient wireless communication, as the more information the sensors convey the more precise actuation becomes. However wireless communication is constrained by the inherent uncertainty of the wireless medium as well as resource limitations at the devices, e.g., limited power resources. The increased number of wireless devices in such environments further necessitates the management of the shared wireless spectrum with direct account of control performance. To address these challenges, the goal of this work is to provide control-aware and resource-aware communication policies. This is first examined in the fundamental problem of allocating transmit power resources for wireless closed loop control. Opportunistic online adaptation of power to plant and wireless channel conditions is shown to be essential in achieving the optimal tradeoff between control performance and power utilization. Optimal structural properties of channel access mechanisms are also considered for the problem of guaranteeing multiple control performance requirements over a shared wireless medium. This includes scheduling mechanisms implemented by central authorities, as well as decentralized mechanisms implemented independently by the wireless devices with emerging wireless interferences. Again the mechanisms exhibit an opportunistic adaptation to varying wireless channel conditions, especially designed to explore the tradeoffs between different communication links and meet control performance requirements. The structural characterization is augmented with tractable optimization algorithms to compute these channel access mechanisms. Finally, as control is naturally a dynamic task that requires a long term planning, appropriate dynamic algorithms adapting to the varying control system states are examined. Besides adapting dynamically, the proposed algorithms provide guarantees about long term control performance and resource utilization by construction
Robust optimization for adversarial learning with finite sample complexity guarantees
Decision making and learning in the presence of
uncertainty has attracted significant attention in view of the
increasing need to achieve robust and reliable operations.
In the case where uncertainty stems from the presence of
adversarial attacks this need is becoming more prominent.
In this paper we focus on linear and nonlinear classification
problems and propose a novel adversarial training method
for robust classifiers, inspired by Support Vector Machine
(SVM) margins. We view robustness under a data driven lens,
and derive finite sample complexity bounds for both linear
and non-linear classifiers in binary and multi-class scenarios.
Notably, our bounds match natural classifiers’ complexity. Our
algorithm minimizes a worst-case surrogate loss using Linear
Programming (LP) and Second Order Cone Programming
(SOCP) for linear and non-linear models. Numerical experiments
on the benchmark MNIST and CIFAR10 datasets show our
approach’s comparable performance to state-of-the-art methods,
without needing adversarial examples during training. Our work
offers a comprehensive framework for enhancing binary linear
and non-linear classifier robustness, embedding robustness in
learning under the presence of adversaries
Resilient Monotone Submodular Function Maximization
In this paper, we focus on applications in machine learning, optimization,
and control that call for the resilient selection of a few elements, e.g.
features, sensors, or leaders, against a number of adversarial
denial-of-service attacks or failures. In general, such resilient optimization
problems are hard, and cannot be solved exactly in polynomial time, even though
they often involve objective functions that are monotone and submodular.
Notwithstanding, in this paper we provide the first scalable,
curvature-dependent algorithm for their approximate solution, that is valid for
any number of attacks or failures, and which, for functions with low curvature,
guarantees superior approximation performance. Notably, the curvature has been
known to tighten approximations for several non-resilient maximization
problems, yet its effect on resilient maximization had hitherto been unknown.
We complement our theoretical analyses with supporting empirical evaluations.Comment: Improved suboptimality guarantees on proposed algorithm and corrected
typo on Algorithm 1's statemen
Statistical Learning for Analysis of Networked Control Systems over Unknown Channels
Recent control trends are increasingly relying on communication networks and
wireless channels to close the loop for Internet-of-Things applications.
Traditionally these approaches are model-based, i.e., assuming a network or
channel model they are focused on stability analysis and appropriate controller
designs. However the availability of such wireless channel modeling is
fundamentally challenging in practice as channels are typically unknown a
priori and only available through data samples. In this work we aim to develop
algorithms that rely on channel sample data to determine the stability and
performance of networked control tasks. In this regard our work is the first to
characterize the amount of channel modeling that is required to answer such a
question. Specifically we examine how many channel data samples are required in
order to answer with high confidence whether a given networked control system
is stable or not. This analysis is based on the notion of sample complexity
from the learning literature and is facilitated by concentration inequalities.
Moreover we establish a direct relation between the sample complexity and the
networked system stability margin, i.e., the underlying packet success rate of
the channel and the spectral radius of the dynamics of the control system. This
illustrates that it becomes impractical to verify stability under a large range
of plant and channel configurations. We validate our theoretical results in
numerical simulations