160 citations · 618 across the 56 of their papers we have counts for
7 papers · 1 filter
Controlled privacy leakage propagation throughout overlapping grouped learning
Shahrzad Kiani, Franziska Boenisch, Stark C. Draper
Federated Learning (FL) is the standard protocol for collaborative learning. In FL, multiple workers jointly train a shared model. They exchange model updates calculated on their d…
Differentially Private Federated Learning With Time-Adaptive Privacy Spending
Shahrzad Kiani, Nupur Kulkarni, Adam Dziedzic +2
Federated learning (FL) with differential privacy (DP) provides a framework for collaborative machine learning, enabling clients to train a shared model while adhering to strict pr…
Compressing gradients by exploiting temporal correlation in momentum-SGD
Tharindu B. Adikari, Stark C. Draper
An increasing bottleneck in decentralized optimization is communication. Bigger models and growing datasets mean that decentralization of computation is important and that the amou…
Node-Centric Graph Learning from Data for Brain State Identification
Nafiseh Ghoroghchian, David M. Groppe, Roman Genov +2
Data-driven graph learning models a network by determining the strength of connections between its nodes. The data refers to a graph signal which associates a value with each graph…
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization
Nuwan Ferdinand, Haider Al-Lawati, Stark C. Draper +1
Distributed optimization is vital in solving large-scale machine learning problems. A widely-shared feature of distributed optimization techniques is the requirement that all nodes…
Efficient learning of neighbor representations for boundary trees and forests
Tharindu Adikari, Stark C. Draper
We introduce a semiparametric approach to neighbor-based classification. We build off the recently proposed Boundary Trees algorithm by Mathy et al.(2015) which enables fast neighb…