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Found 20 papers, 7 papers with code
Adaptive Federated Learning Over the Air
In contrast, an Adam-like algorithm converges at the $\mathcal{O}( 1/T )$ rate, demonstrating its advantage in expediting the model training process.
Spectral Co-Distillation for Personalized Federated Learning
Personalized federated learning (PFL) has been widely investigated to address the challenge of data heterogeneity, especially when a single generic model is inadequate in satisfying the diverse performance requirements of local clients simultaneously.
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data
Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from decentralized local clients manifests a globally prevalent long-tailed distribution, has garnered considerable attention in recent times.
Personalized Federated Learning with Attention-based Client Selection
FedACS integrates an attention mechanism to enhance collaboration among clients with similar data distributions and mitigate the data scarcity issue.
Value of Information and Timing-aware Scheduling for Federated Learning
Federated Learning (FL) offers a solution by preserving data privacy during training.
Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer
Data privacy and long-tailed distribution are the norms rather than the exception in many real-world tasks.
The Role of Federated Learning in a Wireless World with Foundation Models
Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications.
Stand for Something or Fall for Everything: Predict Misinformation Spread with Stance-Aware Graph Neural Networks
Although pervasive spread of misinformation on social media platforms has become a pressing challenge, existing platform interventions have shown limited success in curbing its dissemination.
Modeling Inverse Demand Function with Explainable Dual Neural Networks
Financial contagion has been widely recognized as a fundamental risk to the financial system.
Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
Federated edge learning is envisioned as the bedrock of enabling intelligence in next-generation wireless networks, but the limited spectral resources often constrain its scalability.
ChatGPT Informed Graph Neural Network for Stock Movement Prediction
However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier.
Personalizing Federated Learning with Over-the-Air Computations
Additionally, we leverage a bi-level optimization framework to personalize the federated learning model so as to cope with the data heterogeneity issue.
Imperceptible Adversarial Attack via Invertible Neural Networks
Adding perturbations via utilizing auxiliary gradient information or discarding existing details of the benign images are two common approaches for generating adversarial examples.
Semantics-Preserving Sketch Embedding for Face Generation
With a semantic feature matching loss for effective semantic supervision, our sketch embedding precisely conveys the semantics in the input sketches to the synthesized images.
Towards Federated Long-Tailed Learning
Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks.
FedCorr: Multi-Stage Federated Learning for Label Noise Correction
Although there exist methods in centralized learning for tackling label noise, such methods do not perform well on heterogeneous label noise in FL settings, due to the typically smaller sizes of client datasets and data privacy requirements in FL.
Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering
We showed that unsupervised clustering of image patches could help identify predictive patches, exclude patches lack of predictive information, and therefore improve prediction on gene mutations in all three different cancer types, compared with the WSI based method without selection of image patches and models based on only tumor regions.
Dynamic Attention-based Communication-Efficient Federated Learning
Federated learning (FL) offers a solution to train a global machine learning model while still maintaining data privacy, without needing access to data stored locally at the clients.
Sentiment Analysis of the COVID-related r/Depression Posts
In this study, we analyzed sentiments of COVID-related messages posted on r/Depression.
DeepFacePencil: Creating Face Images from Freehand Sketches
In this paper, we explore the task of generating photo-realistic face images from hand-drawn sketches.
