all InfoSec news
FedComm: Federated Learning as a Medium for Covert Communication. (arXiv:2201.08786v1 [cs.CR])
Jan. 24, 2022, 2:20 a.m. | Dorjan Hitaj, Giulio Pagnotta, Briland Hitaj, Fernando Perez-Cruz, Luigi V. Mancini
cs.CR updates on arXiv.org arxiv.org
Proposed as a solution to mitigate the privacy implications related to the
adoption of deep learning solutions, Federated Learning (FL) enables large
numbers of participants to successfully train deep neural networks without
having to reveal the actual private training data. To date, a substantial
amount of research has investigated the security and privacy properties of FL,
resulting in a plethora of innovative attack and defense strategies. This paper
thoroughly investigates the communication capabilities of an FL scheme. In
particular, we …
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Cybersecurity Skills Challenge -- Sponsored by DoD
@ Correlation One | United States
Security Operations Center (SOC) Analyst
@ GK Cybersecurity Group | Remote
Azure Security Architect
@ First Quality | Remote US - Eastern or Central Timezone
Senior Security Engineer
@ LRQA | Birmingham, GB, B37 7ES
Product Security Intern
@ Sinch | Chicago, Illinois, United States
Cyber Support Engineer
@ Darktrace | New York