Search Results for author:
Found 8 papers, 3 papers with code
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
Recent methods addressed the client data heterogeneity issue via personalized federated learning (PFL) - a class of FL algorithms aiming to personalize learned global knowledge to better suit the clients' local data distributions.
Adaptive Test-Time Personalization for Federated Learning
To tackle this challenge, we propose a novel algorithm called ATP to adaptively learns the adaptation rates for each module in the model from distribution shifts among source domains.
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
We systematically review related works from the data to the computational aspects.
Optimizing the Collaboration Structure in Cross-Silo Federated Learning
In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized.
Understanding the Effect of Data Augmentation on Knowledge Distillation
However, such data augmentation methods are sub-optimal for knowledge distillation since the teacher model could provide label distributions and is more tolerant to semantic shifts.
BOBA: Byzantine-Robust Federated Learning with Label Skewness
In federated learning, most existing robust aggregation rules (AGRs) combat Byzantine attacks in the IID setting, where client data is assumed to be independent and identically distributed.
On the Importance of Architecture and Feature Selection in Differentially Private Machine Learning
The use of differentially private learning algorithms in a "drop-in" fashion -- without accounting for the impact of differential privacy (DP) noise when choosing what feature engineering operations to use, what features to select, or what neural network architecture to use -- yields overly complex and poorly performing models.
Deep Grouping Model for Unified Perceptual Parsing
The perceptual-based grouping process produces a hierarchical and compositional image representation that helps both human and machine vision systems recognize heterogeneous visual concepts.
