35 citations · 107 across the 26 of their papers we have counts for
29 papers
Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise
Antesh Upadhyay, Abolfazl Hashemi
We propose an improved convergence analysis technique that characterizes the distributed learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink commu…
Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications
Ege C. Kaya, M. Berk Sahin, Abolfazl Hashemi
This paper focuses on a multi-agent zeroth-order online optimization problem in a federated learning setting for target tracking. The agents only sense their current distances to t…
Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data
Sai Aparna Aketi, Abolfazl Hashemi, Kaushik Roy
Decentralized learning enables the training of deep learning models over large distributed datasets generated at different locations, without the need for a central server. However…
On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing
Vishnu Pandi Chellapandi, Antesh Upadhyay, Abolfazl Hashemi +1
Decentralized learning and optimization is a central problem in control that encompasses several existing and emerging applications, such as federated learning. While there exists…
Geo-mechanical aspects for breakage detachment of rock fines by Darcys flow
Abolfazl Hashemi, Sara Borazjani, Cuong Nguyen +9
Suspension-colloidal-nano transport in porous media encompasses the detachment of detrital fines against electrostatic attraction and authigenic fines by breakage, from the rock su…
No-Regret Learning in Dynamic Stackelberg Games
Niklas Lauffer, Mahsa Ghasemi, Abolfazl Hashemi +2
In a Stackelberg game, a leader commits to a randomized strategy, and a follower chooses their best strategy in response. We consider an extension of a standard Stackelberg game, c…
On the Benefits of Inducing Local Lipschitzness for Robust Generative Adversarial Imitation Learning
Farzan Memarian, Abolfazl Hashemi, Scott Niekum +1
We explore methodologies to improve the robustness of generative adversarial imitation learning (GAIL) algorithms to observation noise. Towards this objective, we study the effect…
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
Anish Acharya, Abolfazl Hashemi, Prateek Jain +3
Geometric median (\textsc{Gm}) is a classical method in statistics for achieving a robust estimation of the uncorrupted data; under gross corruption, it achieves the optimal breakd…
On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates
Rudrajit Das, Abolfazl Hashemi, Sujay Sanghavi +1
There is a dearth of convergence results for differentially private federated learning (FL) with non-Lipschitz objective functions (i.e., when gradient norms are not bounded). The…
Generalization Bounds for Sparse Random Feature Expansions
Abolfazl Hashemi, Hayden Schaeffer, Robert Shi +3
Random feature methods have been successful in various machine learning tasks, are easy to compute, and come with theoretical accuracy bounds. They serve as an alternative approach…
Physical-Layer Security via Distributed Beamforming in the Presence of Adversaries with Unknown Locations
Yagiz Savas, Abolfazl Hashemi, Abraham P. Vinod +2
We study the problem of securely communicating a sequence of information bits with a client in the presence of multiple adversaries at unknown locations in the environment. We assu…
Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs
Yiyue Chen, Abolfazl Hashemi, Haris Vikalo
We consider the problem of decentralized optimization over time-varying directed networks. The network nodes can access only their local objectives, and aim to collaboratively mini…
Faster Non-Convex Federated Learning via Global and Local Momentum
Rudrajit Das, Anish Acharya, Abolfazl Hashemi +3
We propose \texttt{FedGLOMO}, a novel federated learning (FL) algorithm with an iteration complexity of to converge to an -stationary point (i.e., $\math…
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization
Abolfazl Hashemi, Anish Acharya, Rudrajit Das +3
In decentralized optimization, it is common algorithmic practice to have nodes interleave (local) gradient descent iterations with gossip (i.e. averaging over the network) steps. M…
Decentralized Optimization On Time-Varying Directed Graphs Under Communication Constraints
Yiyue Chen, Abolfazl Hashemi, Haris Vikalo
We consider the problem of decentralized optimization where a collection of agents, each having access to a local cost function, communicate over a time-varying directed network an…
Identifying Sparse Low-Dimensional Structures in Markov Chains: A Nonnegative Matrix Factorization Approach
Mahsa Ghasemi, Abolfazl Hashemi, Haris Vikalo +1
We consider the problem of learning low-dimensional representations for large-scale Markov chains. We formulate the task of representation learning as that of mapping the state spa…
Performance-Complexity Tradeoffs in Greedy Weak Submodular Maximization with Random Sampling
Abolfazl Hashemi, Haris Vikalo, Gustavo de Veciana
Many problems in signal processing and machine learning can be formalized as weak submodular optimization tasks. For such problems, a simple greedy algorithm (\textsc{Greedy}) is g…
Submodular Observation Selection and Information Gathering for Quadratic Models
Abolfazl Hashemi, Mahsa Ghasemi, Haris Vikalo +1
We study the problem of selecting most informative subset of a large observation set to enable accurate estimation of unknown parameters. This problem arises in a variety of settin…
Evolutionary Self-Expressive Models for Subspace Clustering
Abolfazl Hashemi, Haris Vikalo
The problem of organizing data that evolves over time into clusters is encountered in a number of practical settings. We introduce evolutionary subspace clustering, a method whose…
Randomized Greedy Sensor Selection: Leveraging Weak Submodularity
Abolfazl Hashemi, Mahsa Ghasemi, Haris Vikalo +1
We study the problem of estimating a random process from the observations collected by a network of sensors that operate under resource constraints. When the dynamics of the proces…
Near-Optimal Distributed Estimation for a Network of Sensing Units Operating Under Communication Constraints
Abolfazl Hashemi, Osman Fatih Kilic, Haris Vikalo
We study the problem of distributed state estimation in a network of sensing units that can exchange their measurements but the rate of communication between the units is constrain…
A Novel Scheme for Support Identification and Iterative Sampling of Bandlimited Graph Signals
Abolfazl Hashemi, Rasoul Shafipour, Haris Vikalo +1
We study the problem of sampling and reconstruction of bandlimited graph signals where the objective is to select a node subset of prescribed cardinality that ensures interpolation…
Towards Accelerated Greedy Sampling and Reconstruction of Bandlimited Graph Signals
Abolfazl Hashemi, Rasoul Shafipour, Haris Vikalo +1
We study the problem of sampling and reconstructing spectrally sparse graph signals where the objective is to select a subset of nodes of prespecified cardinality that ensures inte…
Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation
Abolfazl Hashemi, Rasoul Shafipour, Haris Vikalo +1
We study the problem of sampling a bandlimited graph signal in the presence of noise, where the objective is to select a node subset of prescribed cardinality that minimizes the si…