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07/27/2023
Network Fault-tolerant and Byzantine-resilient Social Learning via Collaborative Hierarchical Non-Bayesian Learning
As the network scale increases, existing fully distributed solutions sta...
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06/29/2023
Fast and Robust State Estimation and Tracking via Hierarchical Learning
Fully distributed estimation and tracking solutions to large-scale multi...
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06/01/2023
Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications
Federated learning (FL) is a decentralized learning framework wherein a ...
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05/31/2023
Federated Learning in the Presence of Adversarial Client Unavailability
Federated learning is a decentralized machine learning framework wherein...
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03/08/2023
Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles
Deep learning is the method of choice for trajectory prediction for auto...
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01/18/2023
Nonlocalization of singular potentials in quantum dynamics
Nonlocal modeling has drawn more and more attention and becomes steadily...
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10/03/2022
β-Stochastic Sign SGD: A Byzantine Resilient and Differentially Private Gradient Compressor for Federated Learning
Federated Learning (FL) is a nascent privacy-preserving learning framewo...
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06/15/2022
Global Convergence of Federated Learning for Mixed Regression
This paper studies the problem of model training under Federated Learnin...
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01/13/2022
Experimental Design Networks: A Paradigm for Serving Heterogeneous Learners under Networking Constraints
Significant advances in edge computing capabilities enable learning to o...
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08/22/2021
The Power of Random Symmetry-Breaking in Nakamoto Consensus
Nakamoto consensus underlies the security of many of the world's largest...
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06/29/2021
Achieving Statistical Optimality of Federated Learning: Beyond Stationary Points
Federated Learning (FL) is a promising framework that has great potentia...
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05/26/2019
On Learning Over-parameterized Neural Networks: A Functional Approximation Prospective
We consider training over-parameterized two-layer neural networks with R...
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04/21/2019
Spike-Based Winner-Take-All Computation: Fundamental Limits and Order-Optimal Circuits
Winner-Take-All (WTA) refers to the neural operation that selects a (typ...
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01/07/2019
Distributed Learning with Adversarial Agents Under Relaxed Network Condition
This work studies the problem of non-Bayesian learning over multi-agent ...
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11/08/2018
Collaboratively Learning the Best Option on Graphs, Using Bounded Local Memory
We consider multi-armed bandit problems in social groups wherein each in...
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04/26/2018
Securing Distributed Machine Learning in High Dimensions
We consider securing a distributed machine learning system wherein the d...
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02/22/2018
Collaboratively Learning the Best Option, Using Bounded Memory
We consider multi-armed bandit problems in social groups wherein each in...
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05/16/2017
