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Found 9 papers, 2 papers with code
Adversarial Plannning
We evaluate the performance of the algorithms against two dominant planning algorithms used in commercial applications (D* Lite and Fast Downward) and show both are vulnerable to extremely limited adversarial action.
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization
Then, using the derived convergence bound, we use stochastic optimization to develop a new client selection and power allocation algorithm that minimizes a function of the convergence bound and the average communication time under a transmit power constraint.
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
A Pareto GAN leverages extreme value theory and the functional properties of neural networks to learn a distribution that matches the asymptotic behavior of the marginal distributions of the features.
Topological Effects on Attacks Against Vertex Classification
Vertex classification is vulnerable to perturbations of both graph topology and vertex attributes, as shown in recent research.
MaxHedge: Maximising a Maximum Online
We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set.
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches.
Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices
In doing so Augur tackles several challenges: (i) how to overcome pro ling and measurement overhead; (ii) how to capture the variance in different mobile platforms with different processors, memory, and cache sizes; and (iii) how to account for the variance in the number, type and size of layers of the different CNN configurations.
Influence of Personal Preferences on Link Dynamics in Social Networks
We find that personal preferences, in particular political views, and preferences for common activities help predict link formation and dissolution in both the behavioral and cognitive networks.
Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process
In mobile edge computing, local edge servers can host cloud-based services, which reduces network overhead and latency but requires service migrations as users move to new locations.
Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Optimization and Control
