most citedTowards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach

1 citations · 1 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CR2024

TRUST: A Toolkit for TEE-Assisted Secure Outsourced Computation over Integers

Bowen Zhao, Jiuhui Li, Peiming Xu +3

Secure outsourced computation (SOC) provides secure computing services by taking advantage of the computation power of cloud computing and the technology of privacy computing (e.g.…

cs.LG2024★ 1 cited

Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach

Xiaojing Chen, Zhenyuan Li, Wei Ni +5

Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its multip…

cs.CR2023

SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers

Bowen Zhao, Weiquan Deng, Xiaoguo Li +3

Secure outsourced computation is critical for cloud computing to safeguard data confidentiality and ensure data usability. Recently, secure outsourced computation schemes following…