35 citations · 107 across the 26 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
Accelerated Sparse Subspace Clustering
Abolfazl Hashemi, Haris Vikalo
State-of-the-art algorithms for sparse subspace clustering perform spectral clustering on a similarity matrix typically obtained by representing each data point as a sparse combina…