Projects

Secure Large-Scale Serveless Training at the Edge
Developed a fast and computationally efficient Byzantine robust algorithm that leverages a sequential, memory assisted and performance criteria for training over a logical ring.
Preprint
Secure Large-Scale Serveless Training at the Edge
Fast and Robust Large-Scale Distributed Gradient Descent
Developed a practical algorithm for distributed learning that is both communication efficient and straggler-resilient.
ISIT T-ON
Fast and Robust Large-Scale Distributed Gradient Descent
Mitigating Byzantine Attacks in Federated Learning
Proposed a novel sampling based approach that applies per client criteria for mitigating Byzantines in the general federated learning setting.
Preprint
Mitigating Byzantine Attacks in Federated Learning
Low-Latency Federated Learning in Wireless Edge Networks
Proposed CodedFedL that injects structured coding redundancy into non-linear federated learning for mitigating stragglers and speeding up training procedure in heterogeneous MEC networks.
Globecom ICML Workshop JSAC
Low-Latency Federated Learning in Wireless Edge Networks
Hierarchical Decentralized Training at the Edge
Formulated a problem for decentralized training from data at the edge users, incorporating the challenges of straggling communications and limited communication bandwidth.
ISIT Longer
Hierarchical Decentralized Training at the Edge
Communciation Efficient Large-Scale Graph Processing
Proposed and implemented a practical MapReduce based approach for large-scale graph processing.
ISIT T-IT
Communciation Efficient Large-Scale Graph Processing
Pre-defined Sparsity for Convolutional Neural Networks
Proposed the first approach to reduce footprint of convolutional neural networks via pre-defined sparsity.
Allerton
Pre-defined Sparsity for Convolutional Neural Networks
Optimal Resource Allocation for Cloud Computing
Developed an efficient approach for load allocation in heterogeneous cloud clusters.
ISIT T-IT
Optimal Resource Allocation for Cloud Computing