6 citations · 25 across the 16 of their papers we have counts for
18 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.…
Rateless Codes for Low-Latency Distributed Inference in Mobile Edge Computing
Anton Frigård, Siddhartha Kumar, Eirik Rosnes +1
We consider a mobile edge computing scenario where users want to perform a linear inference operation on local data for some networ…
Multi-Server Weakly-Private Information Retrieval
Hsuan-Yin Lin, Siddhartha Kumar, Eirik Rosnes +2
Private information retrieval (PIR) protocols ensure that a user can download a file from a database without revealing any information on the identity of the requested file to the…
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…