4 citations · 5 across the 3 of their papers we have counts for
4 papers
CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning
Reent Schlegel, Siddhartha Kumar, Eirik Rosnes +1
We present two novel federated learning (FL) schemes that mitigate the effect of straggling devices by introducing redundancy on the devices' data across the network. Compared to o…
Privacy-Preserving Coded Mobile Edge Computing for Low-Latency Distributed Inference
Reent Schlegel, Siddhartha Kumar, Eirik Rosnes +1
We consider a mobile edge computing scenario where a number of devices want to perform a linear inference on some local data given a…
Coding for Straggler Mitigation in Federated Learning
Siddhartha Kumar, Reent Schlegel, Eirik Rosnes +1
We present a novel coded federated learning (FL) scheme for linear regression that mitigates the effect of straggling devices while retaining the privacy level of conventional FL.…
Private Edge Computing for Linear Inference Based on Secret Sharing
Reent Schlegel, Siddhartha Kumar, Eirik Rosnes +1
We consider an edge computing scenario where users want to perform a linear computation on local, private data and a network-wide, public matrix. Users offload computations to edge…