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Found 35 papers, 1 papers with code
Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization
Classically, aggregation in distributed learning is based on averaging, which is statistically efficient, but susceptible to attacks by even a small number of malicious agents.
Exact Subspace Diffusion for Decentralized Multitask Learning
Classical paradigms for distributed learning, such as federated or decentralized gradient descent, employ consensus mechanisms to enforce homogeneity among agents.
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.
Distributed Bayesian Learning of Dynamic States
This work studies networked agents cooperating to track a dynamical state of nature under partial information.
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.
Networked Signal and Information Processing
Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources.
Quantization for decentralized learning under subspace constraints
In this paper, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces.
Robust and Efficient Aggregation for Distributed Learning
This is followed by an aggregation step, which traditionally takes the form of a (weighted) average.
Dencentralized learning in the presence of low-rank noise
Observations collected by agents in a network may be unreliable due to observation noise or interference.
Explainability and Graph Learning from Social Interactions
Social learning algorithms provide models for the formation of opinions over social networks resulting from local reasoning and peer-to-peer exchanges.
Optimal Aggregation Strategies for Social Learning over Graphs
Adaptive social learning is a useful tool for studying distributed decision-making problems over graphs.
Privatized Graph Federated Learning
Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model.
Online Graph Learning from Social Interactions
For a given graph topology, these algorithms allow for the prediction of formed opinions.
Learning from Heterogeneous Data Based on Social Interactions over Graphs
In the proposed social machine learning (SML) strategy, two phases are present: in the training phase, classifiers are independently trained to generate a belief over a set of hypotheses using a finite number of training samples; in the prediction phase, classifiers evaluate streaming unlabeled observations and share their instantaneous beliefs with neighboring classifiers.
Hidden Markov Modeling over Graphs
This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks.
Competing Adaptive Networks
Adaptive networks have the capability to pursue solutions of global stochastic optimization problems by relying only on local interactions within neighborhoods.
Deception in Social Learning
Then, we will explain that it is possible for such attacks to succeed by showing that strategies exist that can be adopted by the malicious agents for this purpose.
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.
Social learning under inferential attacks
A common assumption in the social learning literature is that agents exchange information in an unselfish manner.
Graph-Homomorphic Perturbations for Private Decentralized Learning
Decentralized algorithms for stochastic optimization and learning rely on the diffusion of information as a result of repeated local exchanges of intermediate estimates.
Network Classifiers Based on Social Learning
Combination over time means that the classifiers respond to streaming data during testing and continue to improve their performance even during this phase.
Dif-MAML: Decentralized Multi-Agent Meta-Learning
The formalism of meta-learning is actually well-suited to this decentralized setting, where the learner would be able to benefit from information and computational power spread across the agents.
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.
Second-Order Guarantees in Centralized, Federated and Decentralized Nonconvex Optimization
Rapid advances in data collection and processing capabilities have allowed for the use of increasingly complex models that give rise to nonconvex optimization problems.
Dynamic Federated Learning
Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments.
Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning
Multitask learning is an approach to inductive transfer learning (using what is learned for one problem to assist in another problem) and helps improve generalization performance relative to learning each task separately by using the domain information contained in the training signals of related tasks as an inductive bias.
Linear Speedup in Saddle-Point Escape for Decentralized Non-Convex Optimization
Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (in the number of agents) relative to non-cooperative approaches in the strongly-convex setting.
Regularized Diffusion Adaptation via Conjugate Smoothing
The purpose of this work is to develop and study a distributed strategy for Pareto optimization of an aggregate cost consisting of regularized risks.
Second-Order Guarantees of Stochastic Gradient Descent in Non-Convex Optimization
Recent years have seen increased interest in performance guarantees of gradient descent algorithms for non-convex optimization.
Distributed Learning in Non-Convex Environments -- Part II: Polynomial Escape from Saddle-Points
In Part I [2] of this work we established that agents cluster around a network centroid and proceeded to study the dynamics of this point.
Stochastic Learning under Random Reshuffling with Constant Step-sizes
In empirical risk optimization, it has been observed that stochastic gradient implementations that rely on random reshuffling of the data achieve better performance than implementations that rely on sampling the data uniformly.
