Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Jayaram, P R"'
This paper presents the design and implementation of FLIPS, a middleware system to manage data and participant heterogeneity in federated learning (FL) training workloads. In particular, we examine the benefits of label distribution clustering on par
Externí odkaz:
http://arxiv.org/abs/2308.03901
The increasing number and scale of federated learning (FL) jobs necessitates resource efficient scheduling and management of aggregation to make the economics of cloud-hosted aggregation work. Existing FL research has focused on the design of FL algo
Externí odkaz:
http://arxiv.org/abs/2208.09740
Advances in federated learning (FL) algorithms,along with technologies like differential privacy and homomorphic encryption, have led to FL being increasingly adopted and used in many application domains. This increasing adoption has led to rapid gro
Externí odkaz:
http://arxiv.org/abs/2203.12163
Autor:
Cheng, Pau-Chen, Eykholt, Kevin, Gu, Zhongshu, Jamjoom, Hani, Jayaram, K. R., Valdez, Enriquillo, Verma, Ashish
Federated Learning (FL) enables collaborative training among mutually distrusting parties. Model updates, rather than training data, are concentrated and fused in a central aggregation server. A key security challenge in FL is that an untrustworthy o
Externí odkaz:
http://arxiv.org/abs/2105.09400
Federated learning enables multiple, distributed participants (potentially on different clouds) to collaborate and train machine/deep learning models by sharing parameters/gradients. However, sharing gradients, instead of centralizing data, may not b
Externí odkaz:
http://arxiv.org/abs/2012.00740
The increased use of deep learning (DL) in academia, government and industry has, in turn, led to the popularity of on-premise and cloud-hosted deep learning platforms, whose goals are to enable organizations utilize expensive resources effectively,
Externí odkaz:
http://arxiv.org/abs/2006.13878
Autor:
Jayaram, K. R., Muthusamy, Vinod, Dube, Parijat, Ishakian, Vatche, Wang, Chen, Herta, Benjamin, Boag, Scott, Arroyo, Diana, Tantawi, Asser, Verma, Archit, Pollok, Falk, Khalaf, Rania
Deep learning (DL) is becoming increasingly popular in several application domains and has made several new application features involving computer vision, speech recognition and synthesis, self-driving automobiles, drug design, etc. feasible and acc
Externí odkaz:
http://arxiv.org/abs/1909.06526
Autor:
Jayaram, K. R.
Publikováno v:
Middleware 2013. Lecture Notes in Computer Science, vol 8275. Springer, Berlin, Heidelberg
For distributed applications to take full advantage of cloud computing systems, we need middleware systems that allow developers to build elasticity management components right into the applications. This paper describes the design and implementation
Externí odkaz:
http://arxiv.org/abs/1909.03346
Autor:
Boag, Scott, Dube, Parijat, Maghraoui, Kaoutar El, Herta, Benjamin, Hummer, Waldemar, Jayaram, K. R., Khalaf, Rania, Muthusamy, Vinod, Kalantar, Michael, Verma, Archit
Deep learning (DL), a form of machine learning, is becoming increasingly popular in several application domains. As a result, cloud-based Deep Learning as a Service (DLaaS) platforms have become an essential infrastructure in many organizations. Thes
Externí odkaz:
http://arxiv.org/abs/1805.06801
Autor:
Bhattacharjee, Bishwaranjan, Boag, Scott, Doshi, Chandani, Dube, Parijat, Herta, Ben, Ishakian, Vatche, Jayaram, K. R., Khalaf, Rania, Krishna, Avesh, Li, Yu Bo, Muthusamy, Vinod, Puri, Ruchir, Ren, Yufei, Rosenberg, Florian, Seelam, Seetharami R., Wang, Yandong, Zhang, Jian Ming, Zhang, Li
Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision. At the same time, the "as-a-Service"-based business mo
Externí odkaz:
http://arxiv.org/abs/1709.05871