Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Maßny, Luis"'
We tackle the problem of Byzantine errors in distributed gradient descent within the Byzantine-resilient gradient coding framework. Our proposed solution can recover the exact full gradient in the presence of $s$ malicious workers with a data replica
Externí odkaz:
http://arxiv.org/abs/2401.16915
We consider gradient coding in the presence of an adversary controlling so-called malicious workers trying to corrupt the computations. Previous works propose the use of MDS codes to treat the responses from malicious workers as errors and correct th
Externí odkaz:
http://arxiv.org/abs/2401.02380
We consider gradient coding in the presence of an adversary, controlling so-called malicious workers trying to corrupt the computations. Previous works propose the use of MDS codes to treat the inputs of the malicious workers as errors and correct th
Externí odkaz:
http://arxiv.org/abs/2303.13231
We consider distributed learning in the presence of slow and unresponsive worker nodes, referred to as stragglers. In order to mitigate the effect of stragglers, gradient coding redundantly assigns partial computations to the worker such that the ove
Externí odkaz:
http://arxiv.org/abs/2212.08580
Autor:
Maßny, Luis, Wachter-Zeh, Antonia
Over-the-air computation has the potential to increase the communication-efficiency of data-dependent distributed wireless systems, but is vulnerable to eavesdropping. We consider over-the-air computation over block-fading additive white Gaussian noi
Externí odkaz:
http://arxiv.org/abs/2212.04288
Autor:
Saha, Souradip1 (AUTHOR) souradip.saha@fkie.fraunhofer.de, Maßny, Luis2 (AUTHOR), Adrat, Marc1 (AUTHOR) souradip.saha@fkie.fraunhofer.de, Jax, Peter2 (AUTHOR)
Publikováno v:
Entropy. Oct2022, Vol. 24 Issue 10, p1457-N.PAG. 16p.
