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Found 10 papers, 0 papers with code
Impact of Redundancy on Resilience in Distributed Optimization and Learning
In particular, we introduce the notion of $(f, r; \epsilon)$-resilience to characterize how well the true solution is approximated in the presence of up to $f$ Byzantine faulty agents, and up to $r$ slow agents (or stragglers) -- smaller $\epsilon$ represents a better approximation.
Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent
We consider the problem of Byzantine fault-tolerance in the peer-to-peer (P2P) distributed gradient-descent method -- a prominent algorithm for distributed optimization in a P2P system.
Distributed Optimization
Distributed, Parallel, and Cluster Computing
Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE)
We show that the CGE gradient-filter guarantees fault-tolerance against a bounded fraction of Byzantine agents under standard stochastic assumptions, and is computationally simpler compared to many existing gradient-filters such as multi-KRUM, geometric median-of-means, and the spectral filters.
Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning
The coding schemes use the concept of reactive redundancy for isolating Byzantine workers that eventually send faulty information.
Byzantine Fault Tolerant Distributed Linear Regression
This paper considers the problem of Byzantine fault tolerance in distributed linear regression in a multi-agent system.
Private Learning on Networks: Part II
This paper considers a distributed multi-agent optimization problem, with the global objective consisting of the sum of local objective functions of the agents.
Private Learning on Networks
In a distributed machine learning scenario, the dataset is stored among several machines and they solve a distributed optimization problem to collectively learn the underlying model.
Distributed Optimization of Convex Sum of Non-Convex Functions
We present a distributed solution to optimizing a convex function composed of several non-convex functions.
Distributed Optimization for Client-Server Architecture with Negative Gradient Weights
We present an algorithm for the recently proposed multi-parameter-server architecture.
Defending Non-Bayesian Learning against Adversarial Attacks
This paper addresses the problem of non-Bayesian learning over multi-agent networks, where agents repeatedly collect partially informative observations about an unknown state of the world, and try to collaboratively learn the true state.
