140 citations · 293 across the 25 of their papers we have counts for
28 papers
Network Fault-tolerant and Byzantine-resilient Social Learning via Collaborative Hierarchical Non-Bayesian Learning
Connor Mclaughlin, Matthew Ding, Denis Edogmus +1
As the network scale increases, existing fully distributed solutions start to lag behind the real-world challenges such as (1) slow information propagation, (2) network communicati…
Fast and Robust State Estimation and Tracking via Hierarchical Learning
Connor Mclaughlin, Matthew Ding, Deniz Erdogmus +1
Fast and reliable state estimation and tracking are essential for real-time situation awareness in Cyber-Physical Systems (CPS) operating in tactical environments or complicated ci…
Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications
Ming Xiang, Stratis Ioannidis, Edmund Yeh +2
Federated learning (FL) is a decentralized learning framework wherein a parameter server (PS) and a collection of clients collaboratively train a model via minimizing a global obje…
Federated Learning in the Presence of Adversarial Client Unavailability
Lili Su, Ming Xiang, Jiaming Xu +1
Federated learning is a decentralized machine learning framework that enables collaborative model training without revealing raw data. Due to the diverse hardware and software limi…
Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles
Muzi Peng, Jiangwei Wang, Dongjin Song +2
Deep learning is the method of choice for trajectory prediction for autonomous vehicles. Unfortunately, its data-hungry nature implicitly requires the availability of sufficiently…
Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithms
Ming Xiang, Lili Su
Federated Learning (FL) is a nascent decentralized learning framework under which a massive collection of heterogeneous clients collaboratively train a model without revealing thei…
Global Convergence of Federated Learning for Mixed Regression
Lili Su, Jiaming Xu, Pengkun Yang
This paper studies the problem of model training under Federated Learning when clients exhibit cluster structure. We contextualize this problem in mixed regression, where each clie…
Experimental Design Networks: A Paradigm for Serving Heterogeneous Learners under Networking Constraints
Yuezhou Liu, Yuanyuan Li, Lili Su +2
Significant advances in edge computing capabilities enable learning to occur at geographically diverse locations. In general, the training data needed in those learning tasks are n…
The Power of Random Symmetry-Breaking in Nakamoto Consensus
Lili Su, Quanquan C. Liu, Neha Narula
Nakamoto consensus underlies the security of many of the world's largest cryptocurrencies, such as Bitcoin and Ethereum. Common lore is that Nakamoto consensus only achieves consis…
A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points
Lili Su, Jiaming Xu, Pengkun Yang
Federated Learning (FL) is a promising decentralized learning framework and has great potentials in privacy preservation and in lowering the computation load at the cloud. Recent w…
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective
Lili Su, Pengkun Yang
We consider training over-parameterized two-layer neural networks with Rectified Linear Unit (ReLU) using gradient descent (GD) method. Inspired by a recent line of work, we study…
Spike-Based Winner-Take-All Computation: Fundamental Limits and Order-Optimal Circuits
Lili Su, Chia-Jung Chang, Nancy Lynch
Winner-Take-All (WTA) refers to the neural operation that selects a (typically small) group of neurons from a large neuron pool. It is conjectured to underlie many of the brain's f…
Distributed Learning with Adversarial Agents Under Relaxed Network Condition
Pooja Vyavahare, Lili Su, Nitin H. Vaidya
This work studies the problem of non-Bayesian learning over multi-agent network when there are some adversarial (faulty) agents in the network. At each time step, each non-faulty a…
Collaboratively Learning the Best Option on Graphs, Using Bounded Local Memory
Lili Su, Martin Zubeldia, Nancy Lynch
We consider multi-armed bandit problems in social groups wherein each individual has bounded memory and shares the common goal of learning the best arm/option. We say an individual…
Finite-time Guarantees for Byzantine-Resilient Distributed State Estimation with Noisy Measurements
Lili Su, Shahin Shahrampour
This work considers resilient, cooperative state estimation in unreliable multi-agent networks. A network of agents aims to collaboratively estimate the value of an unknown vector…
Securing Distributed Gradient Descent in High Dimensional Statistical Learning
Lili Su, Jiaming Xu
We consider unreliable distributed learning systems wherein the training data is kept confidential by external workers, and the learner has to interact closely with those workers t…
Collaboratively Learning the Best Option, Using Bounded Memory
Lili Su, Martin Zubeldia, Nancy Lynch
We consider multi-armed bandit problems in social groups wherein each individual has bounded memory and shares the common goal of learning the best arm/option. We say an individual…
Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent
Yudong Chen, Lili Su, Jiaming Xu
We consider the problem of distributed statistical machine learning in adversarial settings, where some unknown and time-varying subset of working machines may be compromised and b…
Defending Non-Bayesian Learning against Adversarial Attacks
Lili Su, Nitin H. Vaidya
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 th…
On the Convergence Rate of Average Consensus and Distributed Optimization over Unreliable Networks
Lili Su
We consider the problems of reaching average consensus and solving consensus-based optimization over unreliable communication networks wherein packets may be dropped accidentally d…
Asynchronous Distributed Hypothesis Testing in the Presence of Crash Failures
Lili Su, Nitin H. Vaidya
This paper addresses the problem of distributed hypothesis testing in multi-agent networks, where agents repeatedly collect local observations about an unknown state of the world,…
Fault-Tolerant Distributed Optimization (Part IV): Constrained Optimization with Arbitrary Directed Networks
Lili Su, Nitin H. Vaidya
We study the problem of constrained distributed optimization in multi-agent networks when some of the computing agents may be faulty. In this problem, the system goal is to have al…
Fault-Tolerant Multi-Agent Optimization: Part III
Lili Su, Nitin Vaidya
We study fault-tolerant distributed optimization of a sum of convex (cost) functions with real-valued scalar input/output in the presence of crash faults or Byzantine faults. In pa…
Byzantine Multi-Agent Optimization: Part I
Lili Su, Nitin Vaidya
We study Byzantine fault-tolerant distributed optimization of a sum of convex (cost) functions with real-valued scalar input/ouput. In particular, the goal is to optimize a global…