Saurav Prakash
Saurav Prakash
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Privacy-Preserving and Robust Machine Learning at the Edge
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
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
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
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.
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