15 citations · 29 across the 8 of their papers we have counts for
10 papers
FLIPS: Federated Learning using Intelligent Participant Selection
Rahul Atul Bhope, K. R. Jayaram, Nalini Venkatasubramanian +2
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 parti…
Just-in-Time Aggregation for Federated Learning
K. R. Jayaram, Ashish Verma, Gegi Thomas +1
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 aggre…
Adaptive Aggregation For Federated Learning
K. R. Jayaram, Vinod Muthusamy, Gegi Thomas +2
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 m…
Separation of Powers in Federated Learning
Pau-Chen Cheng, Kevin Eykholt, Zhongshu Gu +4
Federated Learning (FL) enables collaborative training among mutually distrusting parties. Model updates, rather than training data, are concentrated and fused in a central aggrega…
MYSTIKO : : Cloud-Mediated, Private, Federated Gradient Descent
K. R. Jayaram, Archit Verma, Ashish Verma +2
Federated learning enables multiple, distributed participants (potentially on different clouds) to collaborate and train machine/deep learning models by sharing parameters/gradient…
Effective Elastic Scaling of Deep Learning Workloads
Vaibhav Saxena, K. R. Jayaram, Saurav Basu +2
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…