Search Results for author:
Found 8 papers, 3 papers with code
Distributed In-Context Learning under Non-IID Among Clients
Especially when the client data are non-identical independent distributions (non-IID), retrieving from clients a proper set of ICEs needed for a test query presents critical challenges.
Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization
In the medical domain, collecting data from multiple sites or institutions is a common strategy that guarantees sufficient clinical diversity, determined by the decentralized nature of medical data.
FedNoisy: Federated Noisy Label Learning Benchmark
Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients.
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network
Sufficient dimension reduction is a powerful tool to extract core information hidden in the high-dimensional data and has potentially many important applications in machine learning tasks.
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning
However, privacy information could be leaked from uploaded gradients and be exposed to malicious attackers or an honest-but-curious server.
Interacting Contour Stochastic Gradient Langevin Dynamics
We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (CSGLD) sampler with efficient interactions.
Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm
The algorithm consists of a Fourier accumulation SAFT (FA-SAFT) and a directional model-based (D-MB) deconvolution method.
FedLab: A Flexible Federated Learning Framework
Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations.
