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Found 10 papers, 9 papers with code
PFLlib: Personalized Federated Learning Algorithm Library
Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm that allows collaborative learning with data privacy protection, personalized FL (pFL) has gained significant prominence as a research direction within the FL domain.
Eliminating Domain Bias for Federated Learning in Representation Space
Recently, federated learning (FL) is popular for its privacy-preserving and collaborative learning abilities.
GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning
Federated Learning (FL) is popular for its privacy-preserving and collaborative learning capabilities.
FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy
To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively.
Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
Recently, Diffusion Models (DMs) boost a wave in AI for Art yet raise new copyright concerns, where infringers benefit from using unauthorized paintings to train DMs to generate novel paintings in a similar style.
FedALA: Adaptive Local Aggregation for Personalized Federated Learning
A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client.
Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization
Bayesian neural networks have been widely used in many applications because of the distinctive probabilistic representation framework.
Self-Supervised Vessel Segmentation via Adversarial Learning
Vessel segmentation is critically essential for diagnosinga series of diseases, e. g., coronary artery disease and retinal disease.
Quantifying consensus of rankings based on q-support patterns
Existing studies are insufficient in assessing the overall consensus of a ranking set.
A Novel Fuzzy Search Approach over Encrypted Data with Improved Accuracy and Efficiency
To avoid privacy leaks, outsourced data usually is encrypted before being sent to cloud servers, which disables traditional search schemes for plain text.
