2 code implementations • 18 Sep 2017 • Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi, Parijat Dube, Ben Herta, Vatche Ishakian, K. R. Jayaram, Rania Khalaf, Avesh Krishna, Yu Bo Li, Vinod Muthusamy, Ruchir Puri, Yufei Ren, Florian Rosenberg, Seetharami R. Seelam, Yandong Wang, Jian Ming Zhang, Li Zhang
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.
Distributed, Parallel, and Cluster Computing
no code implementations • 14 Sep 2019 • K. R. Jayaram, Vinod Muthusamy, Parijat Dube, Vatche Ishakian, Chen Wang, Benjamin Herta, Scott Boag, Diana Arroyo, Asser Tantawi, Archit Verma, Falk Pollok, Rania Khalaf
This paper describes the design, implementation and our experiences with FfDL, a DL platform used at IBM.
no code implementations • 24 Jun 2020 • Vaibhav Saxena, K. R. Jayaram, Saurav Basu, Yogish Sabharwal, Ashish Verma
We design a fast dynamic programming based optimizer to solve this problem in real-time to determine jobs that can be scaled up/down, and use this optimizer in an autoscaler to dynamically change the allocated resources and batch sizes of individual DL jobs.
no code implementations • 1 Dec 2020 • K. R. Jayaram, Archit Verma, Ashish Verma, Gegi Thomas, Colin Sutcher-Shepard
Federated learning enables multiple, distributed participants (potentially on different clouds) to collaborate and train machine/deep learning models by sharing parameters/gradients.
no code implementations • 19 May 2021 • Pau-Chen Cheng, Kevin Eykholt, Zhongshu Gu, Hani Jamjoom, K. R. Jayaram, Enriquillo Valdez, Ashish Verma
In this paper, we introduce TRUDA, a new cross-silo FL system, employing a trustworthy and decentralized aggregation architecture to break down information concentration with regard to a single aggregator.
no code implementations • 7 Aug 2023 • Rahul Atul Bhope, K. R. Jayaram, Nalini Venkatasubramanian, Ashish Verma, Gegi Thomas
In particular, we examine the benefits of label distribution clustering on participant selection in federated learning.