all InfoSec news
Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation. (arXiv:2209.04599v1 [cs.CR])
Sept. 13, 2022, 1:20 a.m. | Xuan Gong, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, Arun Innanje
cs.CR updates on arXiv.org arxiv.org
Federated Learning (FL) is a machine learning paradigm where local nodes
collaboratively train a central model while the training data remains
decentralized. Existing FL methods typically share model parameters or employ
co-distillation to address the issue of unbalanced data distribution. However,
they suffer from communication bottlenecks. More importantly, they risk privacy
leakage. In this work, we develop a privacy preserving and communication
efficient method in a FL framework with one-shot offline knowledge distillation
using unlabeled, cross-domain public data. We propose …
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Information Technology Specialist II: Network Architect
@ Los Angeles County Employees Retirement Association (LACERA) | Pasadena, CA
Cybersecurity Skills Challenge -- Sponsored by DoD
@ Correlation One | United States
Security Operations Center (SOC) Analyst
@ GK Cybersecurity Group | Remote
Engineering Manager - Cloud Security team
@ SentinelOne | Prague, Czech Republic
Legal & Compliance Apprentice (H/F)
@ Novo Nordisk | Puteaux, Île-de-France, FR
Manager, Governance Risk & Compliance
@ Comcast | Virtual