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Found 13 papers, 3 papers with code
Effective Image Tampering Localization via Enhanced Transformer and Co-attention Fusion
Powerful manipulation techniques have made digital image forgeries be easily created and widespread without leaving visual anomalies.
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence
With the obtained FL hyperparameters and resource allocation, we design a MAML-based FL algorithm, called Automated Federated Learning (AutoFL), that is able to conduct fast adaptation and convergence.
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks
Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks.
Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples via Least-Squares Solution
The experiments show that our proposed algorithm takes only about 400 seconds to handle up to 9. 6 million large-scale samples, while the state-of-the-art algorithms take close to 1000 seconds to handle every 1000 samples, which embodies the advantage of our algorithms in handling large-scale samples. δ -data indistinguishability theory, we provide quantitative theoretical guarantees for the security of our algorithms.
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations
In edge computing, users' service profiles are migrated due to user mobility.
Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks
Experimental results verify the effectiveness of PerFedS2 in saving training time as well as guaranteeing the convergence of training loss, in contrast to synchronous and asynchronous PFL algorithms.
Secure Forward Aggregation for Vertical Federated Neural Networks
In this paper, we focus on SplitNN, a well-known neural network framework in VFL, and identify a trade-off between data security and model performance in SplitNN.
Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond.
Practical and Secure Federated Recommendation with Personalized Masks
In this paper, we proposed federated masked matrix factorization (FedMMF) to protect the data privacy in federated recommender systems without sacrificing efficiency and effectiveness.
Joint Network Topology Inference via Structured Fusion Regularization
Theoretically, we provide a theoretical analysis of the proposed graph estimator, which establishes a non-asymptotic bound of the estimation error under the high-dimensional setting and reflects the effect of several key factors on the convergence rate of our algorithm.
Fresh, Fair and Energy-Efficient Content Provision in a Private and Cache-Enabled UAV Network
Particularly, we first propose to decompose the sequential decision problem into multiple repeated optimization subproblems via a Lyapunov technique.
Networking and Internet Architecture Signal Processing
Disentangling Homophemes in Lip Reading using Perplexity Analysis
This paper proposes a method to tackle the one-to-many mapping problem when performing automated lip reading using solely visual cues in two separate scenarios: the first scenario is where the word boundary, that is, the beginning and the ending of a word, is unknown; and the second scenario is where the boundary is known.
Multi-Armed Bandit Based Client Scheduling for Federated Learning
In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels.
