Zobrazeno 1 - 10
of 138
pro vyhledávání: '"Gauthier, Francois"'
This paper develops a networked federated learning algorithm to solve nonsmooth objective functions. To guarantee the confidentiality of the participants with respect to each other and potential eavesdroppers, we use the zero-concentrated differentia
Externí odkaz:
http://arxiv.org/abs/2306.14012
Autor:
Gauthier, Francois, Gogineni, Vinay Chakravarthi, Werner, Stefan, Huang, Yih-Fang, Kuh, Anthony
Publikováno v:
IEEE Transactions on Signal and Information Processing over Networks (2023) 1-14
This paper presents a personalized graph federated learning (PGFL) framework in which distributedly connected servers and their respective edge devices collaboratively learn device or cluster-specific models while maintaining the privacy of every ind
Externí odkaz:
http://arxiv.org/abs/2306.06399
Autor:
Gauthier, Francois, Gogineni, Vinay Chakravarthi, Werner, Stefan, Huang, Yih-Fang, Kuh, Anthony
Publikováno v:
IEEE Internet of Things Journal (2023)
Online federated learning (FL) enables geographically distributed devices to learn a global shared model from locally available streaming data. Most online FL literature considers a best-case scenario regarding the participating clients and the commu
Externí odkaz:
http://arxiv.org/abs/2303.15226
Autor:
Gauthier, Francois, Bae, Sora
Untrusted deserialization exploits, where a serialised object graph is used to achieve denial-of-service or arbitrary code execution, have become so prominent that they were introduced in the 2017 OWASP Top 10. In this paper, we present a novel and l
Externí odkaz:
http://arxiv.org/abs/2204.09388
Autor:
Gauthier, Francois, Gogineni, Vinay Chakravarthi, Werner, Stefan, Huang, Yih-Fang, Kuh, Anthony
Many assumptions in the federated learning literature present a best-case scenario that can not be satisfied in most real-world applications. An asynchronous setting reflects the realistic environment in which federated learning methods must be able
Externí odkaz:
http://arxiv.org/abs/2111.13931
Autor:
Gauthier, François, Hassanshahi, Behnaz, Selwyn-Smith, Benjamin, Mai, Trong Nhan, Schlüter, Max, Williams, Micah
Following the advent of the American Fuzzy Lop (AFL), fuzzing had a surge in popularity, and modern day fuzzers range from simple blackbox random input generators to complex whitebox concolic frameworks that are capable of deep program introspection.
Externí odkaz:
http://arxiv.org/abs/2108.08455
Over the years, static taint analysis emerged as the analysis of choice to detect some of the most common web application vulnerabilities, such as SQL injection (SQLi) and cross-site scripting (XSS)~\cite{OWASP}. Furthermore, from an implementation p
Externí odkaz:
http://arxiv.org/abs/2103.16240
Autor:
Cifuentes, Cristina, Gauthier, François, Hassanshahi, Behnaz, Krishnan, Padmanabhan, McCall, Davin
Publikováno v:
In Computers & Security December 2023 135
The popularity of the PDF format and the rich JavaScript environment that PDF viewers offer make PDF documents an attractive attack vector for malware developers. PDF documents present a serious threat to the security of organizations because most us
Externí odkaz:
http://arxiv.org/abs/1810.12490
Autor:
Brent, Lexi, Jurisevic, Anton, Kong, Michael, Liu, Eric, Gauthier, Francois, Gramoli, Vincent, Holz, Ralph, Scholz, Bernhard
The rise of modern blockchains has facilitated the emergence of smart contracts: autonomous programs that live and run on the blockchain. Smart contracts have seen a rapid climb to prominence, with applications predicted in law, business, commerce, a
Externí odkaz:
http://arxiv.org/abs/1809.03981