Zobrazeno 1 - 10
of 5 363
pro vyhledávání: '"Kliewer, A."'
We consider the problem of collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. In particular, we provide a
Externí odkaz:
http://arxiv.org/abs/2411.07094
Autor:
Bakshi, Mayank, Ghasvarianjahromi, Sara, Yakimenka, Yauhen, Beemer, Allison, Kosut, Oliver, Kliewer, Joerg
We introduce the paradigm of validated decentralized learning for undirected networks with heterogeneous data and possible adversarial infiltration. We require (a) convergence to a global empirical loss minimizer when adversaries are absent, and (b)
Externí odkaz:
http://arxiv.org/abs/2405.07316
Autor:
Chou, Remi A., Kliewer, Joerg
Consider the problem of storing data in a distributed manner over $T$ servers. Specifically, the data needs to (i) be recoverable from any $\tau$ servers, and (ii) remain private from any $z$ colluding servers, where privacy is quantified in terms of
Externí odkaz:
http://arxiv.org/abs/2403.10676
In 2018, Yang et al. introduced a novel and effective approach, using maximum distance separable (MDS) codes, to mitigate the impact of elasticity in cloud computing systems. This approach is referred to as coded elastic computing. Some limitations o
Externí odkaz:
http://arxiv.org/abs/2401.12151
Autor:
Khanduri, Prashant, Li, Chengyin, Sultan, Rafi Ibn, Qiang, Yao, Kliewer, Joerg, Zhu, Dongxiao
Recently, compositional optimization (CO) has gained popularity because of its applications in distributionally robust optimization (DRO) and many other machine learning problems. Large-scale and distributed availability of data demands the developme
Externí odkaz:
http://arxiv.org/abs/2311.12652
Public transport is vital for meeting people's mobility needs. Providers need to plan their services well to offer high quality and low cost. Optimized planning can benefit providers, customers, and municipalities. The planning process for public tra
Externí odkaz:
http://arxiv.org/abs/2310.13425
Publikováno v:
in Proc. IEEE Inf. Theory Workshop (ITW), Visby, Sweden, Aug. 25-28, 2019, pp. 1-5
We consider the problem of private computation (PC) in a distributed storage system. In such a setting a user wishes to compute a function of $f$ messages replicated across $n$ noncolluding databases, while revealing no information about the desired
Externí odkaz:
http://arxiv.org/abs/2307.01772
Autor:
Kliewer, Brandon W.1 (AUTHOR) bkliewer@ksu.edu
Publikováno v:
Adult Learning. Nov2024, Vol. 35 Issue 4, p228-238. 11p.
We consider the straggler problem in decentralized learning over a logical ring while preserving user data privacy. Especially, we extend the recently proposed framework of differential privacy (DP) amplification by decentralization by Cyffers and Be
Externí odkaz:
http://arxiv.org/abs/2212.03080
Autor:
Obead, Sarah A., Kliewer, Jörg
We formulate a new variant of the private information retrieval (PIR) problem where the user is pliable, i.e., interested in any message from a desired subset of the available dataset, denoted as pliable private information retrieval (PPIR). We consi
Externí odkaz:
http://arxiv.org/abs/2206.05759