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06/11/2023
Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates
With the increasing demand for large-scale training of machine learning ...
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06/06/2023
A Lightweight Method for Tackling Unknown Participation Probabilities in Federated Averaging
In federated learning (FL), clients usually have diverse participation p...
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05/10/2023
Collaborative Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud Network
Kubernetes (k8s) has the potential to coordinate distributed edge resour...
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04/17/2023
Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation
Incentive mechanism is crucial for federated learning (FL) when rational...
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04/10/2023
Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning
Predictions made by deep learning models are prone to data perturbations...
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01/23/2023
FedExP: Speeding up Federated Averaging Via Extrapolation
Federated Averaging (FedAvg) remains the most popular algorithm for Fede...
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12/16/2022
Federated Learning with Flexible Control
Federated learning (FL) enables distributed model training from local da...
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08/26/2022
Flexible Vertical Federated Learning with Heterogeneous Parties
We propose Flexible Vertical Federated Learning (Flex-VFL), a distribute...
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06/16/2022
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data
We propose Compressed Vertical Federated Learning (C-VFL) for communicat...
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05/26/2022
A Unified Analysis of Federated Learning with Arbitrary Client Participation
Federated learning (FL) faces challenges of intermittent client availabi...
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04/13/2022
Joint Coreset Construction and Quantization for Distributed Machine Learning
Coresets are small, weighted summaries of larger datasets, aiming at pro...
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01/19/2022
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization
Federated learning (FL) is a useful tool in distributed machine learning...
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01/03/2022
KerGNNs: Interpretable Graph Neural Networks with Graph Kernels
Graph kernels are historically the most widely-used technique for graph ...
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12/21/2021
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling
Federated learning (FL) algorithms usually sample a fraction of clients ...
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08/19/2021
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning
We consider federated learning in tiered communication networks. Our net...
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02/08/2021
Communication-efficient k-Means for Edge-based Machine Learning
We consider the problem of computing the k-means centers for a large hig...
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01/17/2021
Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System
Kubernetes (k8s) has the potential to merge the distributed edge and the...
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11/07/2020
Robustness and Diversity Seeking Data-Free Knowledge Distillation
Knowledge distillation (KD) has enabled remarkable progress in model com...
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10/24/2020
Local Averaging Helps: Hierarchical Federated Learning and Convergence Analysis
Federated learning is an effective approach to realize collaborative lea...
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07/06/2020
Sharing Models or Coresets: A Study based on Membership Inference Attack
Distributed machine learning generally aims at training a global model b...
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07/06/2020
Online Learning of Facility Locations
In this paper, we provide a rigorous theoretical investigation of an onl...
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02/13/2020
Online Algorithms for Multi-shop Ski Rental with Machine Learned Predictions
We study the problem of augmenting online algorithms with machine learne...
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01/22/2020
Data Selection for Federated Learning with Relevant and Irrelevant Data at Clients
Federated learning is an effective way of training a machine learning mo...
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01/14/2020
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
Federated learning (FL) is an emerging technique for training machine le...
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09/26/2019
Model Pruning Enables Efficient Federated Learning on Edge Devices
Federated learning is a recent approach for distributed model training w...
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08/16/2019
Distilling On-Device Intelligence at the Network Edge
Devices at the edge of wireless networks are the last mile data sources ...
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06/17/2019
Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems
Mobile edge computing (MEC) is a promising technique for providing low-l...
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05/22/2019
Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems
Large-scale distributed computing systems often contain thousands of dis...
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04/11/2019
Robust Coreset Construction for Distributed Machine Learning
Motivated by the need of solving machine learning problems over distribu...
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10/28/2018
MaxHedge: Maximising a Maximum Online with Theoretical Performance Guarantees
We introduce a new online learning framework where, at each trial, the l...
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04/14/2018