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2023/10/16 · We propose a novel approximation algorithm for federated submodular maximization by incorporating client-level DP mechanisms and decomposed function ...
2024/01/15 · Abstract—Submodular maximization is a fundamental problem in many Internet of Things applications, such as sensor.
2024/10/22 · We propose a two-phase framework which computes the average value while preserving heterogeneous privacy for nodes' private data. The new ...
関連する質問
What is the difference between federated learning and differential privacy?
What are the weakness of differential privacy?
2024. Federated Submodular Maximization with Differential Privacy. IEEE Internet of Things Journal, 11(2):1827-1839. [Link]. 2023. Xin Zhou, Kun Tian, Zihan ...
2024/06/03 · There are several models of privacy and security that have been considered in the literature such as Differential Privacy (DP) and Secure ...
Shuffled Model of Differential Privacy in Federated Learning; Private ... Differentially Private Monotone Submodular Maximization Under Matroid and Knapsack ...
There are several models of privacy and security that have been considered in the literature such as Differential Privacy (DP) and Secure. Aggregator (SecAgg), ...
2024/01/31 · We show that our federated algorithm is guaranteed to provide a good approximate solution, even in the presence of above cost-cutting measures.
In every communication round of federated learning, a random subset of clients communicate their model updates back to the server which then aggregates them.