no code implementations • FL4NLP (ACL) 2022 • Huili Chen, Jie Ding, Eric Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang
Inspired by Bayesian hierarchical models, we develop ActPerFL, a self-aware personalized FL method where each client can automatically balance the training of its local personal model and the global model that implicitly contributes to other clients’ training.
no code implementations • 5 May 2024 • Feiyang Kang, Hoang Anh Just, Yifan Sun, Himanshu Jahagirdar, Yuanzhi Zhang, Rongxing Du, Anit Kumar Sahu, Ruoxi Jia
The goal is to minimize the need for costly domain-specific data for subsequent fine-tuning while achieving desired performance levels.
1 code implementation • 20 Oct 2023 • Marco Bornstein, Amrit Singh Bedi, Anit Kumar Sahu, Furqan Khan, Furong Huang
On real-world data, RealFM improves device and server utility, as well as data contribution, by over 3 and 4 magnitudes respectively compared to baselines.
no code implementations • 3 Aug 2023 • Guruprasad V Ramesh, Gopinath Chennupati, Milind Rao, Anit Kumar Sahu, Ariya Rastrow, Jasha Droppo
Federated Learning (FL) is a privacy-preserving paradigm, allowing edge devices to learn collaboratively without sharing data.
no code implementations • 21 Jun 2023 • Milind Rao, Gopinath Chennupati, Gautam Tiwari, Anit Kumar Sahu, Anirudh Raju, Ariya Rastrow, Jasha Droppo
Automatic speech recognition (ASR) models with low-footprint are increasingly being deployed on edge devices for conversational agents, which enhances privacy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
no code implementations • 21 Jun 2023 • Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, Andreas Stolcke
We show the efficacy of our approach using LibriSpeech and LibriLight benchmarks and find an improvement of 4 to 25\% over baselines that uniformly weight all the experts, use a single expert model, or combine experts using ROVER.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
no code implementations • 19 Jul 2022 • Gopinath Chennupati, Milind Rao, Gurpreet Chadha, Aaron Eakin, Anirudh Raju, Gautam Tiwari, Anit Kumar Sahu, Ariya Rastrow, Jasha Droppo, Andy Oberlin, Buddha Nandanoor, Prahalad Venkataramanan, Zheng Wu, Pankaj Sitpure
For end-to-end automatic speech recognition (ASR) tasks, the absence of human annotated labels along with the need for privacy preserving policies for model building makes it a daunting challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
no code implementations • 22 Jun 2022 • Amrit Singh Bedi, Chen Fan, Alec Koppel, Anit Kumar Sahu, Brian M. Sadler, Furong Huang, Dinesh Manocha
In this work, we quantitatively calibrate the performance of global and local models in federated learning through a multi-criterion optimization-based framework, which we cast as a constrained program.
no code implementations • 17 Apr 2022 • Huili Chen, Jie Ding, Eric Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang
In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives may not be exactly aligned.
no code implementations • 6 Apr 2022 • Dusan Jakovetic, Dragana Bajovic, Anit Kumar Sahu, Soummya Kar, Nemanja Milosevic, Dusan Stamenkovic
We introduce a general framework for nonlinear stochastic gradient descent (SGD) for the scenarios when gradient noise exhibits heavy tails.
no code implementations • 1 Feb 2022 • Jie Ding, Eric Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang
Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs.
no code implementations • 11 Jan 2022 • Sunwoo Lee, Anit Kumar Sahu, Chaoyang He, Salman Avestimehr
We propose a partial model averaging framework that mitigates the model discrepancy issue in Federated Learning.
no code implementations • 29 Jan 2021 • Devin Willmott, Anit Kumar Sahu, Fatemeh Sheikholeslami, Filipe Condessa, Zico Kolter
In this work, we instead show that it is possible to craft (universal) adversarial perturbations in the black-box setting by querying a sequence of different images only once.
3 code implementations • ICLR 2021 • Rizal Fathony, Anit Kumar Sahu, Devin Willmott, J Zico Kolter
Although deep networks are typically used to approximate functions over high dimensional inputs, recent work has increased interest in neural networks as function approximators for low-dimensional-but-complex functions, such as representing images as a function of pixel coordinates, solving differential equations, or representing signed distance fields or neural radiance fields.
1 code implementation • 8 Oct 2020 • Anit Kumar Sahu, Satya Narayan Shukla, J. Zico Kolter
We study the problem of generating adversarial examples in a black-box setting, where we only have access to a zeroth order oracle, providing us with loss function evaluations.
1 code implementation • 13 Jul 2020 • Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter
We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples for deep learning models solely based on information limited to output label~(hard label) to a queried data input.
2 code implementations • 7 Jan 2020 • Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
Federated learning aims to jointly learn statistical models over massively distributed remote devices.
1 code implementation • 30 Sep 2019 • Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter
We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs.
1 code implementation • 27 Sep 2019 • Gaurav Gupta, Anit Kumar Sahu, Wan-Yi Lin
We study the problem of training machine learning models incrementally with batches of samples annotated with noisy oracles.
no code implementations • 25 Sep 2019 • Gaurav Gupta, Anit Kumar Sahu, Wan-Yi Lin
We study the problem of training machine learning models incrementally using active learning with access to imperfect or noisy oracles.
1 code implementation • 21 Aug 2019 • Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized.
4 code implementations • 23 May 2019 • Jianyu Wang, Anit Kumar Sahu, Zhouyi Yang, Gauri Joshi, Soummya Kar
This paper studies the problem of error-runtime trade-off, typically encountered in decentralized training based on stochastic gradient descent (SGD) using a given network.
no code implementations • 18 Mar 2019 • Ran Xin, Anit Kumar Sahu, Usman A. Khan, Soummya Kar
In this paper, we study distributed stochastic optimization to minimize a sum of smooth and strongly-convex local cost functions over a network of agents, communicating over a strongly-connected graph.
22 code implementations • 14 Dec 2018 • Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
Theoretically, we provide convergence guarantees for our framework when learning over data from non-identical distributions (statistical heterogeneity), and while adhering to device-level systems constraints by allowing each participating device to perform a variable amount of work (systems heterogeneity).
no code implementations • 11 Nov 2018 • Anit Kumar Sahu, Shaunak Mishra, Narayan Bhamidipati
The policy based on this state space is trained on past decisions and outcomes via a novel Q-learning algorithm which accounts for the delay in install notifications.
no code implementations • 8 Oct 2018 • Anit Kumar Sahu, Manzil Zaheer, Soummya Kar
This paper focuses on the problem of \emph{constrained} \emph{stochastic} optimization.