Foundations of Coded Computing

Secure and Fault Tolerant Decentralized Learning
Proposed a novel sampling based approach that applies per client criteria for mitigating faults in the general federated learning setting.
Preprint
Secure and Fault Tolerant Decentralized Learning
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
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
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