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Found 33 papers, 8 papers with code
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better
Federated learning (FL) enables distribution of machine learning workloads from the cloud to resource-limited edge devices.
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints
The proposed algorithm is tested in a variety of settings for intermittently available clients under communication constraints, and its efficacy demonstrated on synthetic data and realistically federated benchmarking experiments using CIFAR100 and Shakespeare datasets.
A Graph Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
Reconstructing components of a genomic mixture from data obtained by means of DNA sequencing is a challenging problem encountered in a variety of applications including single individual haplotyping and studies of viral communities.
Sampling Requirements and Accelerated Schemes for Sparse Linear Regression with Orthogonal Least-Squares
We analyze the performance of AOLS and establish lower bounds on the probability of exact recovery for both noiseless and noisy random linear measurements.
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization
In this paper, we show that, in such compressed decentralized optimization settings, there are benefits to having {\em multiple} gossip steps between subsequent gradient iterations, even when the cost of doing so is appropriately accounted for e. g. by means of reducing the precision of compressed information.
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data
A major challenge in federated learning arises when the local data is heterogeneous -- the setting in which performance of the learned global model may deteriorate significantly compared to the scenario where the data is identically distributed across the clients.
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data.
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity
Motivated by high resource costs of centralized machine learning schemes as well as data privacy concerns, federated learning (FL) emerged as an efficient alternative that relies on aggregating locally trained models rather than collecting clients' potentially private data.
Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation
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 signal reconstruction mean squared error (MSE).
Accelerated Sparse Subspace Clustering
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 combination of other points using either basis pursuit (BP) or orthogonal matching pursuit (OMP).
Sparse recovery via Orthogonal Least-Squares under presence of Noise
We consider the Orthogonal Least-Squares (OLS) algorithm for the recovery of a $m$-dimensional $k$-sparse signal from a low number of noisy linear measurements.
Sparse Linear Regression via Generalized Orthogonal Least-Squares
Sparse linear regression, which entails finding a sparse solution to an underdetermined system of linear equations, can formally be expressed as an $l_0$-constrained least-squares problem.
Designing Optimal Mortality Risk Prediction Scores that Preserve Clinical Knowledge
In this paper, we seek to develop risk prediction scores that preserve clinical knowledge embedded in features and structure of the existing additive stepwise scores while addressing limitations caused by variable dichotomization.
Matrix Completion and Performance Guarantees for Single Individual Haplotyping
In this paper, we consider a binary matrix factorization formulation of the single individual haplotyping problem and efficiently solve it by means of alternating minimization.
Evolutionary Self-Expressive Models for Subspace Clustering
The problem of organizing data that evolves over time into clusters is encountered in a number of practical settings.
Performance-Complexity Tradeoffs in Greedy Weak Submodular Maximization with Random Sampling
The latter implies that uniform sampling strategies with a fixed sampling size achieve a non-trivial approximation factor; however, we show that with overwhelming probability, these methods fail to find the optimal subset.
Identifying Sparse Low-Dimensional Structures in Markov Chains: A Nonnegative Matrix Factorization Approach
We formulate the task of representation learning as that of mapping the state space of the model to a low-dimensional state space, called the kernel space.
ComHapDet: A Spatial Community Detection Algorithm for Haplotype Assembly
Results: We propose a novel graphical representation of sequencing reads and pose the haplotype assembly problem as an instance of community detection on a spatial random graph.
Evolutionary Clustering via Message Passing
We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step.
A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)
However, MCML metrics based on model counting show that the performance can degrade substantially when tested against the entire (bounded) input space, indicating the high complexity of precisely learning these properties, and the usefulness of model counting in quantifying the true performance.
Federating Recommendations Using Differentially Private Prototypes
However, in domains that demand protection of personally sensitive data, such as medicine or banking, how can we learn such a model without accessing the sensitive data, and without inadvertently leaking private information?
Communication-Efficient Federated Learning via Optimal Client Sampling
The central server collects updated local models from only the selected clients and combines them with estimated model updates of the clients that were not selected for communication.
Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder
Identification of the type of communication technology and/or modulation scheme based on detected radio signal are challenging problems encountered in a variety of applications including spectrum allocation and radio interference mitigation.
Towards Accelerated Greedy Sampling and Reconstruction of Bandlimited Graph Signals
Then, we consider the Bayesian scenario where we formulate the sampling task as the problem of maximizing a monotone weak submodular function, and propose a randomized-greedy algorithm to find a sub-optimal subset of informative nodes.
Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications
We define a new approach, opportunistic federated learning, in which individual devices belonging to different users seek to learn robust models that are personalized to their user's own experiences.
Decentralized Optimization On Time-Varying Directed Graphs Under Communication Constraints
We propose a communication-efficient algorithm for decentralized convex optimization that rely on sparsification of local updates exchanged between neighboring agents in the network.
Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs
To our knowledge, this is the first decentralized optimization framework for time-varying directed networks that achieves such a convergence rate and applies to settings requiring sparsified communication.
End-to-end system for object detection from sub-sampled radar data
We show robust detection based on radar data reconstructed using 20% of samples under extreme weather conditions such as snow or fog, and on low-illuminated nights.
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation
In particular, each client extracts and sends to the server the means of local data representations and the corresponding soft predictions -- information that we refer to as ``hyper-knowledge".
Automotive RADAR sub-sampling via object detection networks: Leveraging prior signal information
Automotive radar has increasingly attracted attention due to growing interest in autonomous driving technologies.
XVir: A Transformer-Based Architecture for Identifying Viral Reads from Cancer Samples
It is estimated that approximately 15% of cancers worldwide can be linked to viral infections.
Accelerating Non-IID Federated Learning via Heterogeneity-Guided Client Sampling
Statistical heterogeneity of data present at client devices in a federated learning (FL) system renders the training of a global model in such systems difficult.
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
While federated learning (FL) systems often utilize quantization to battle communication and computational bottlenecks, they have heretofore been limited to deploying fixed-precision quantization schemes.
