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Found 13 papers, 0 papers with code
Asynchronous Diffusion Learning with Agent Subsampling and Local Updates
In this work, we examine a network of agents operating asynchronously, aiming to discover an ideal global model that suits individual local datasets.
Decentralized Adversarial Training over Graphs
The vulnerability of machine learning models to adversarial attacks has been attracting considerable attention in recent years.
Multi-Agent Adversarial Training Using Diffusion Learning
This work focuses on adversarial learning over graphs.
Enforcing Privacy in Distributed Learning with Performance Guarantees
We study the privatization of distributed learning and optimization strategies.
Local Graph-homomorphic Processing for Privatized Distributed Systems
We study the generation of dependent random numbers in a distributed fashion in order to enable privatized distributed learning by networked agents.
Privatized Graph Federated Learning
Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model.
A Graph Federated Architecture with Privacy Preserving Learning
Thus in this work, we develop a private multi-server federated learning scheme, which we call graph federated learning.
Federated Learning under Importance Sampling
Federated learning encapsulates distributed learning strategies that are managed by a central unit.
Second-Order Guarantees in Federated Learning
Federated learning is a useful framework for centralized learning from distributed data under practical considerations of heterogeneity, asynchrony, and privacy.
Optimal Importance Sampling for Federated Learning
Federated learning involves a mixture of centralized and decentralized processing tasks, where a server regularly selects a sample of the agents and these in turn sample their local data to compute stochastic gradients for their learning updates.
Tracking Performance of Online Stochastic Learners
The utilization of online stochastic algorithms is popular in large-scale learning settings due to their ability to compute updates on the fly, without the need to store and process data in large batches.
Dynamic Federated Learning
Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments.
Network Classifiers With Output Smoothing
This work introduces two strategies for training network classifiers with heterogeneous agents.
