Search Results for author:
Found 25 papers, 10 papers with code
ActPerFL: Active Personalized Federated Learning
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.
RealFM: A Realistic Mechanism to Incentivize Federated Participation and Contribution
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.
Federated Representation Learning for Automatic Speech Recognition
Federated Learning (FL) is a privacy-preserving paradigm, allowing edge devices to learn collaboratively without sharing data.
Federated Self-Learning with Weak Supervision for Speech Recognition
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
Learning When to Trust Which Teacher for Weakly Supervised ASR
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
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale
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
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus
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.
Self-Aware Personalized Federated Learning
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.
Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise
We introduce a general framework for nonlinear stochastic gradient descent (SGD) for the scenarios when gradient noise exhibits heavy tails.
Federated Learning Challenges and Opportunities: An Outlook
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.
Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits
We propose a partial model averaging framework that mitigates the model discrepancy issue in Federated Learning.
You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries
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.
Multiplicative Filter Networks
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.
Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks
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.
Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes
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.
FedDANE: A Federated Newton-Type Method
Federated learning aims to jointly learn statistical models over massively distributed remote devices.
Black-box Adversarial Attacks with Bayesian Optimization
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.
Noisy Batch Active Learning with Deterministic Annealing
We study the problem of training machine learning models incrementally with batches of samples annotated with noisy oracles.
Learning in Confusion: Batch Active Learning with Noisy Oracle
We study the problem of training machine learning models incrementally using active learning with access to imperfect or noisy oracles.
Federated Learning: Challenges, Methods, and Future Directions
Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized.
MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling
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.
Distributed stochastic optimization with gradient tracking over strongly-connected networks
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.
Federated Optimization in Heterogeneous Networks
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).
Managing App Install Ad Campaigns in RTB: A Q-Learning Approach
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.
Towards Gradient Free and Projection Free Stochastic Optimization
This paper focuses on the problem of \emph{constrained} \emph{stochastic} optimization.
