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Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

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Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy

Nirupam Gupta

In this article, we study the problem of Byzantine fault-tolerance in a federated optimization setting, where there is a group of agents communicating with a centralized coordinator. We allow up to

f

Byzantine-faulty agents, which may not follow a prescr ...

Piscataway2023

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Graph Chatbot

Chattez avec Graph Search

Robust Collaborative Learning with Linear Gradient Overhead

Incremental Learning in Diagonal Linear Networks

Functional data analysis with rough sample paths?

Universal and adaptive methods for robust stochastic optimization

Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy

Towards Understanding Sharpness-Aware Minimization

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Countering Bias in Personalized Rankings From Data Engineering to Algorithm Development

Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem

Robust Collaborative Learning with Linear Gradient Overhead

Universal and adaptive methods for robust stochastic optimization

Incremental Learning in Diagonal Linear Networks

Functional data analysis with rough sample paths?

Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent

Towards Understanding Sharpness-Aware Minimization

Countering Bias in Personalized Rankings From Data Engineering to Algorithm Development

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy