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Found 27 papers, 5 papers with code
Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading
While network coverage maps continue to expand, many devices located in remote areas remain unconnected to terrestrial communication infrastructures, preventing them from getting access to the associated data-driven services.
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks
Our optimization methodology aims to select the best combination of sampled nodes and data offloading configuration to maximize FedL training accuracy while minimizing data processing and D2D communication resource consumption subject to realistic constraints on the network topology and device capabilities.
Edge AI Inference in Heterogeneous Constrained Computing: Feasibility and Opportunities
The network edge's role in Artificial Intelligence (AI) inference processing is rapidly expanding, driven by a plethora of applications seeking computational advantages.
Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization
Our analysis sheds light on the joint impact of device training variables (e. g., number of local gradient descent steps), asynchronous scheduling decisions (i. e., when a device trains a task), and dynamic data drifts on the performance of ML training for different tasks.
Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks
Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks.
Interference Cancellation GAN Framework for Dynamic Channels
Symbol detection is a fundamental and challenging problem in modern communication systems, e. g., multiuser multiple-input multiple-output (MIMO) setting.
Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange
In most of the current literature, FL has been studied for supervised ML tasks, in which edge devices collect labeled data.
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point
CE-FL also introduces floating aggregation point, where the local models generated at the devices and the servers are aggregated at an edge server, which varies from one model training round to another to cope with the network evolution in terms of data distribution and users' mobility.
Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing
However, existing algorithms face issues with slow convergence and/or robustness of performance due to the considerable heterogeneity of data distribution, computation and communication capability at the edge.
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks
PSL considers the realistic scenario where global aggregations are conducted with idle times in-between them for resource efficiency improvements, and incorporates data dispersion and model dispersion with local model condensation into FedL.
Adversarial Neural Networks for Error Correcting Codes
To demonstrate the performance of our framework, we combine it with the very recent neural decoders and show improved performance compared to the original models and traditional decoding algorithms on various codes.
On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge
Edge computing has revolutionized the world of mobile and wireless networks world thanks to its flexible, secure, and performing characteristics.
UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach
The presence of time-varying data heterogeneity and computational resource inadequacy among device clusters motivate four key parts of our methodology: (i) stratified UAV swarms of leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning (HN-PFL), a distributed ML framework for personalized model training across the worker-leader-core network hierarchy, (iii) cooperative UAV resource pooling to address computational inadequacy of devices by conducting model training among the UAV swarms, and (iv) model/concept drift to model time-varying data distributions.
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
We circumvent this challenge by using additional data from proxy distributions learned by advanced generative models.
SSD: A Unified Framework for Self-Supervised Outlier Detection
We demonstrate that SSD outperforms most existing detectors based on unlabeled data by a large margin.
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation
The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server.
RobustBench: a standardized adversarial robustness benchmark
As a research community, we are still lacking a systematic understanding of the progress on adversarial robustness which often makes it hard to identify the most promising ideas in training robust models.
Fast-Convergent Federated Learning
In this paper, we propose a fast-convergent federated learning algorithm, called FOLB, which performs intelligent sampling of devices in each round of model training to optimize the expected convergence speed.
Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks
With increasing expressive power, deep neural networks have significantly improved the state-of-the-art on image classification datasets, such as ImageNet.
From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks
There are several challenges with employing conventional federated learning in contemporary networks, due to the significant heterogeneity in compute and communication capabilities that exist across devices.
AppStreamer: Reducing Storage Requirements of Mobile Games through Predictive Streaming
AppStreamer can, therefore, keep only a small part of the files on the device, akin to a "cache", and download the remainder from a cloud storage server or a nearby edge server when it predicts that the app will need them in the near future.
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples
A large body of recent work has investigated the phenomenon of evasion attacks using adversarial examples for deep learning systems, where the addition of norm-bounded perturbations to the test inputs leads to incorrect output classification.
An Estimation and Analysis Framework for the Rasch Model
The Rasch model is widely used for item response analysis in applications ranging from recommender systems to psychology, education, and finance.
DARTS: Deceiving Autonomous Cars with Toxic Signs
In this paper, we propose and examine security attacks against sign recognition systems for Deceiving Autonomous caRs with Toxic Signs (we call the proposed attacks DARTS).
Linearized Binary Regression
We showcase the efficacy of our methods and results for a number of synthetic and real-world datasets, which demonstrates that linearized binary regression finds potential use in a variety of inference, estimation, signal processing, and machine learning applications that deal with binary-valued observations or measurements.
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos
Our attack pipeline generates adversarial samples which are robust to the environmental conditions and noisy image transformations present in the physical world.
Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization
We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day.
