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Found 46 papers, 23 papers with code
FLASH: Federated Learning Across Simultaneous Heterogeneities
The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data.
Learning Non-myopic Power Allocation in Constrained Scenarios
The problem of optimal power allocation -- for maximizing a given network utility metric -- under instantaneous constraints has recently gained significant popularity.
Plug-and-Play Transformer Modules for Test-Time Adaptation
Parameter-efficient tuning (PET) methods such as LoRA, Adapter, and Visual Prompt Tuning (VPT) have found success in enabling adaptation to new domains by tuning small modules within a transformer model.
Joint channel estimation and data detection in massive MIMO systems based on diffusion models
We propose a joint channel estimation and data detection algorithm for massive multilple-input multiple-output systems based on diffusion models.
Deep Demixing: Reconstructing the Evolution of Network Epidemics
We propose the deep demixing (DDmix) model, a graph autoencoder that can reconstruct epidemics evolving over networks from partial or aggregated temporal information.
Learnable Digital Twin for Efficient Wireless Network Evaluation
Network digital twins (NDTs) facilitate the estimation of key performance indicators (KPIs) before physically implementing a network, thereby enabling efficient optimization of the network configuration.
Learning to Transmit with Provable Guarantees in Wireless Federated Learning
We propose a novel data-driven approach to allocate transmit power for federated learning (FL) over interference-limited wireless networks.
Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks
We develop an efficient and near-optimal solution for beamforming in multi-user multiple-input-multiple-output single-hop wireless ad-hoc interference networks.
Delay-aware Backpressure Routing Using Graph Neural Networks
In this work, we improve upon the widely-used metric of hop distance (and its variants) for the shortest path bias by introducing a bias based on the link duty cycle, which we predict using a graph convolutional neural network.
Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks
Distributed scheduling algorithms for throughput or utility maximization in dense wireless multi-hop networks can have overwhelmingly high overhead, causing increased congestion, energy consumption, radio footprint, and security vulnerability.
Power Allocation for Wireless Federated Learning using Graph Neural Networks
We propose a data-driven approach for power allocation in the context of federated learning (FL) over interference-limited wireless networks.
Delay-Oriented Distributed Scheduling Using Graph Neural Networks
In wireless multi-hop networks, delay is an important metric for many applications.
Link Scheduling using Graph Neural Networks
Test results on medium-sized wireless networks show that our centralized heuristic can reach a near-optimal solution quickly, and our distributed heuristic based on a shallow GCN can reduce by nearly half the suboptimality gap of the distributed greedy solver with minimal increase in complexity.
Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling
On the other hand, we propose a matrix factorization method based on a loss function that generalizes that of the skip-gram model with negative sampling to arbitrary similarity matrices.
Combining Physics and Machine Learning for Network Flow Estimation
The flow estimation problem consists of predicting missing edge flows in a network (e. g., traffic, power and water) based on partial observations.
Identification of Additive Link Metrics: Proof of Selected Theorems
This is a technical report, containing all the theorem proofs in the following two papers: (1) Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley, "Identifiability of Link Metrics Based on End-to-end Path Measurements," in ACM IMC, 2013.
Networking and Internet Architecture
Node Failure Localization: Theorem Proof
This is a technical report, containing all the theorem proofs in paper "On Optimal Monitor Placement for Localizing Node Failures via Network Tomography" by Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung, published in IFIP WG 7. 3 Performance, 2015.
Networking and Internet Architecture
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models
Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in the graph.
Efficient power allocation using graph neural networks and deep algorithm unfolding
We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
Adaptive Contention Window Design using Deep Q-learning
We study the problem of adaptive contention window (CW) design for random-access wireless networks.
Distributed Scheduling using Graph Neural Networks
In small- to middle-sized wireless networks with tens of links, even a shallow GCN-based MWIS scheduler can leverage the topological information of the graph to reduce in half the suboptimality gap of the distributed greedy solver with good generalizability across graphs and minimal increase in complexity.
Deep Demixing: Reconstructing the Evolution of Epidemics Using Graph Neural Networks
We study the temporal reconstruction of epidemics evolving over networks.
Unsupervised Joint $k$-node Graph Representations with Compositional Energy-Based Models
Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in the graph.
Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation
We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
An Extension of Fano's Inequality for Characterizing Model Susceptibility to Membership Inference Attacks
We present a new extension of Fano's inequality and employ it to theoretically establish that the probability of success for a membership inference attack on a deep neural network can be bounded using the mutual information between its inputs and its activations.
Measurement-driven Security Analysis of Imperceptible Impersonation Attacks
The emergence of Internet of Things (IoT) brings about new security challenges at the intersection of cyber and physical spaces.
Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency
There has been a recent surge in research on adversarial perturbations that defeat Deep Neural Networks (DNNs) in machine vision; most of these perturbation-based attacks target object classifiers.
GraphCL: Contrastive Self-Supervised Learning of Graph Representations
We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner.
Topology Inference with Multivariate Cumulants: The Möbius Inference Algorithm
Many tasks regarding the monitoring, management, and design of communication networks rely on knowledge of the routing topology.
A Multifactorial Optimization Paradigm for Linkage Tree Genetic Algorithm
Linkage Tree Genetic Algorithm (LTGA) is an effective Evolutionary Algorithm (EA) to solve complex problems using the linkage information between problem variables.
Attribution-Based Confidence Metric For Deep Neural Networks
These experiments demonstrate the effectiveness of the ABC metric to make DNNs more trustworthy and resilient.
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks
Modern machine learning systems are susceptible to adversarial examples; inputs which clearly preserve the characteristic semantics of a given class, but whose classification is (usually confidently) incorrect.
SENSE: Semantically Enhanced Node Sequence Embedding
Effectively capturing graph node sequences in the form of vector embeddings is critical to many applications.
MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design
In distributed software-defined networks (SDN), multiple physical SDN controllers, each managing a network domain, are implemented to balance centralised control, scalability, and reliability requirements.
Attribution-driven Causal Analysis for Detection of Adversarial Examples
We study the robustness of machine learning models on benign and adversarial inputs in this neighborhood.
Adversarial Perturbations Against Real-Time Video Classification Systems
We exploit recent advances in generative adversarial network (GAN) architectures to account for temporal correlations and generate adversarial samples that can cause misclassification rates of over 80% for targeted activities.
Multi-Armed Bandits on Partially Revealed Unit Interval Graphs
Two settings of complete and partial side information based on whether the UIG is fully revealed are studied and a general two-step learning structure consisting of an offline reduction of the action space and online aggregation of reward observations from similar arms is proposed to fully exploit the topological structure of the side information.
Sparse Diffusion-Convolutional Neural Networks
The predictive power and overall computational efficiency of Diffusion-convolutional neural networks make them an attractive choice for node classification tasks.
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
We study the problem of representation learning in heterogeneous networks.
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Outlier Detection from Network Data with Subnetwork Interpretation
Detecting a small number of outliers from a set of data observations is always challenging.
Modeling Group Dynamics Using Probabilistic Tensor Decompositions
We propose a probabilistic modeling framework for learning the dynamic patterns in the collective behaviors of social agents and developing profiles for different behavioral groups, using data collected from multiple information sources.
Crafting Adversarial Input Sequences for Recurrent Neural Networks
Machine learning models are frequently used to solve complex security problems, as well as to make decisions in sensitive situations like guiding autonomous vehicles or predicting financial market behaviors.
Detection under Privileged Information
In this paper, we consider an alternate learning approach that trains models using "privileged" information--features available at training time but not at runtime--to improve the accuracy and resilience of detection systems.
Practical Black-Box Attacks against Machine Learning
Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN.
The Limitations of Deep Learning in Adversarial Settings
In this work, we formalize the space of adversaries against deep neural networks (DNNs) and introduce a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between inputs and outputs of DNNs.
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
In this work, we introduce a defensive mechanism called defensive distillation to reduce the effectiveness of adversarial samples on DNNs.
