Search Results for author:
Found 4 papers, 1 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.
Vandal: A Scalable Security Analysis Framework for Smart Contracts
In this paper, we present Vandal: a security analysis framework for Ethereum smart contracts.
Programming Languages
