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Found 5 papers, 1 papers with code
Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning
However, since the model is split at a specific layer, known as a cut layer, into both client-side and server-side models for the SFL, the choice of the cut layer in SFL can have a substantial impact on the energy consumption of clients and their privacy, as it influences the training burden and the output of the client-side models.
Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy
This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central server.
Differentially Private Community Detection for Stochastic Block Models
To the best of our knowledge, this is the first work to study the impact of privacy constraints on the fundamental limits for community detection.
Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation
Specifically, the central DP privacy leakage has been shown to scale as $\mathcal{O}(1/K^{1/2})$, where $K$ is the number of users.
Wireless Federated Learning with Local Differential Privacy
In this paper, we study the problem of federated learning (FL) over a wireless channel, modeled by a Gaussian multiple access channel (MAC), subject to local differential privacy (LDP) constraints.
Cryptography and Security Information Theory Information Theory
