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Found 7 papers, 3 papers with code
Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning
We show that these `self-augmentations', i. e. generative augmentations based on the vanilla SSL encoder embeddings, facilitate the training of a stronger SSL encoder.
FedSpaLLM: Federated Pruning of Large Language Models
Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to deploy due to their high computational and storage demands.
Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy.
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2
Federated learning enables multiple data owners to collaboratively train robust machine learning models without transferring large or sensitive local datasets by only sharing the parameters of the locally trained models.
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
Nonetheless, because of the disparity of computing resources among different clients (i. e., device heterogeneity), synchronous federated learning algorithms suffer from degraded efficiency when waiting for straggler clients.
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service
Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e. g., healthcare of financial) local data.
ActiveMatch: End-to-end Semi-supervised Active Representation Learning
With human-in-the-loop, active learning can iteratively select informative unlabeled samples for labeling and training to improve the performance in the SSL framework.
