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Found 6 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.
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.
Differentially Private Distributed Convex Optimization
This paper considers distributed optimization (DO) where multiple agents cooperate to minimize a global objective function, expressed as a sum of local objectives, subject to some constraints.
Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates
Differential privacy (DP) techniques can be applied to the federated learning model to statistically guarantee data privacy against inference attacks to communication among the learning agents.
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning
Federated learning (FL) enables training models at different sites and updating the weights from the training instead of transferring data to a central location and training as in classical machine learning.
Differentially Private Federated Learning via Inexact ADMM
Differential privacy (DP) techniques can be applied to the federated learning model to protect data privacy against inference attacks to communication among the learning agents.
