732 citations · 1.7k across the 44 of their papers we have counts for
7 papers · 1 filter
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers +21
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-s…
Mechanisms that Incentivize Data Sharing in Federated Learning
Sai Praneeth Karimireddy, Wenshuo Guo, Michael I. Jordan
Federated learning is typically considered a beneficial technology which allows multiple agents to collaborate with each other, improve the accuracy of their models, and solve prob…
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy +2
State-of-the-art federated learning methods can perform far worse than their centralized counterparts when clients have dissimilar data distributions. For neural networks, even whe…
Optimization with Access to Auxiliary Information
El Mahdi Chayti, Sai Praneeth Karimireddy
We investigate the fundamental optimization question of minimizing a target function , whose gradients are expensive to compute or have limited availability, given access to som…
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
Ljubomir Rokvic, Panayiotis Danassis, Sai Praneeth Karimireddy +1
In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data. However, traditional data valuation methods are not suitable due to privacy concerns. To a…
Agree to Disagree: Diversity through Disagreement for Better Transferability
Matteo Pagliardini, Martin Jaggi, François Fleuret +1
Gradient-based learning algorithms have an implicit simplicity bias which in effect can limit the diversity of predictors being sampled by the learning procedure. This behavior can…
Byzantine-Robust Decentralized Learning via ClippedGossip
Lie He, Sai Praneeth Karimireddy, Martin Jaggi
In this paper, we study the challenging task of Byzantine-robust decentralized training on arbitrary communication graphs. Unlike federated learning where workers communicate throu…