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Found 5 papers, 3 papers with code
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.
AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI
"Garbage In Garbage Out" is a universally agreed quote by computer scientists from various domains, including Artificial Intelligence (AI).
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.
