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07/25/2023
Scaff-PD: Communication Efficient Fair and Robust Federated Learning
We present Scaff-PD, a fast and communication-efficient algorithm for di...
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06/14/2023
Provably Personalized and Robust Federated Learning
Clustering clients with similar objectives and learning a model per clus...
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06/08/2023
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning
For a federated learning model to perform well, it is crucial to have a ...
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05/27/2023
Federated Conformal Predictors for Distributed Uncertainty Quantification
Conformal prediction is emerging as a popular paradigm for providing rig...
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05/19/2023
Online Learning in a Creator Economy
The creator economy has revolutionized the way individuals can profit th...
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07/13/2022
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
State-of-the-art federated learning methods can perform far worse than t...
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07/10/2022
Mechanisms that Incentivize Data Sharing in Federated Learning
Federated learning is typically considered a beneficial technology which...
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06/01/2022
Optimization with access to auxiliary information
We investigate the fundamental optimization question of minimizing a tar...
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02/09/2022
Agree to Disagree: Diversity through Disagreement for Better Transferability
Gradient-based learning algorithms have an implicit simplicity bias whic...
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02/03/2022
Byzantine-Robust Decentralized Learning via Self-Centered Clipping
In this paper, we study the challenging task of Byzantine-robust decentr...
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11/10/2021
Linear Speedup in Personalized Collaborative Learning
Personalization in federated learning can improve the accuracy of a mode...
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10/28/2021
Towards Model Agnostic Federated Learning Using Knowledge Distillation
An often unquestioned assumption underlying most current federated learn...
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10/08/2021
RelaySum for Decentralized Deep Learning on Heterogeneous Data
In decentralized machine learning, workers compute model updates on thei...
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02/09/2021
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
Decentralized training of deep learning models is a key element for enab...
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12/18/2020
Learning from History for Byzantine Robust Optimization
Byzantine robustness has received significant attention recently given i...
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08/08/2020
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Federated learning is a challenging optimization problem due to the hete...
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08/04/2020
PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning
Lossy gradient compression has become a practical tool to overcome the c...
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06/16/2020
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling
In Byzantine robust distributed optimization, a central server wants to ...
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06/08/2020
Secure Byzantine-Robust Machine Learning
Increasingly machine learning systems are being deployed to edge servers...
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12/06/2019
Why ADAM Beats SGD for Attention Models
While stochastic gradient descent (SGD) is still the de facto algorithm ...
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10/14/2019
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning
Federated learning is a key scenario in modern large-scale machine learn...
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09/11/2019
The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication
We analyze (stochastic) gradient descent (SGD) with delayed updates on s...
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07/11/2019
Amplifying Rényi Differential Privacy via Shuffling
Differential privacy is a useful tool to build machine learning models w...
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05/31/2019
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
We study gradient compression methods to alleviate the communication bot...
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03/20/2019
Accelerating Gradient Boosting Machine
Gradient Boosting Machine (GBM) is an extremely powerful supervised lear...
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01/28/2019
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
Sign-based algorithms (e.g. signSGD) have been proposed as a biased grad...
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10/16/2018
Efficient Greedy Coordinate Descent for Composite Problems
Coordinate descent with random coordinate selection is the current state...
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06/01/2018