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Found 7 papers, 2 papers with code
Enhancing In-context Learning via Linear Probe Calibration
However, applying ICL in real cases does not scale with the number of samples, and lacks robustness to different prompt templates and demonstration permutations.
Single-shot General Hyper-parameter Optimization for Federated Learning
We address the problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
Anonymizing Data for Privacy-Preserving Federated Learning
In this paper, we propose the first syntactic approach for offering privacy in the context of federated learning.
Differential Privacy-enabled Federated Learning for Sensitive Health Data
We demonstrate the feasibility and effectiveness of the federated learning framework in offering an elevated level of privacy and maintaining utility of the global model.
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches.
