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Found 6 papers, 0 papers with code
Personalized Graph Federated Learning with Differential Privacy
Further, our analysis shows that the algorithm ensures local differential privacy for all clients in terms of zero-concentrated differential privacy.
Asynchronous Online Federated Learning with Reduced Communication Requirements
The simulations reveal that in asynchronous settings, the proposed PAO-Fed is able to achieve the same convergence properties as that of the online federated stochastic gradient while reducing the communication overhead by 98 percent.
Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression
In this manner, we reduce the communication load of the participants and, therefore, render participation in the learning task more accessible.
Communication-Efficient Online Federated Learning Framework for Nonlinear Regression
As a solution, this paper presents a partial-sharing-based online federated learning framework (PSO-Fed) that enables clients to update their local models using continuous streaming data and share only portions of those updated models with the server.
Model Approximation Using Cascade of Tree Decompositions
In this paper, we present a general, multistage framework for graphical model approximation using a cascade of models such as trees.
The Quality of the Covariance Selection Through Detection Problem and AUC Bounds
Examples show that the quality of tree approximation models are not good in general based on information divergences, the AUC and its bounds when the number of nodes in the graphical model is large.
