Privacy-Preserving and Robust Machine Learning 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.

Proposed a novel sampling based approach that applies per client criteria for mitigating Byzantines in the general federated learning setting.

Proposed CodedFedL that injects structured coding redundancy into non-linear federated learning for mitigating stragglers and speeding up training procedure in heterogeneous MEC networks.

Formulated a problem for decentralized training from data at the edge users, incorporating the challenges of straggling communications and limited communication bandwidth.