4 papers
FAIRVAR: Fair Federated Learning via Variance Regularization
Zahra Kharaghani, Ali Dadras, Tommy Löfstedt
Federated learning (FL) allows collaborative training of machine learning models across multiple parties without sharing raw data. However, heterogeneous data can cause some client…
Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning
Karlo Palenzuela, Ali Dadras, Alp Yurtsever +1
Multiple local steps are key to communication-efficient federated learning. However, theoretical guarantees for such algorithms, without data heterogeneity-bounding assumptions, ha…
Federated Frank-Wolfe Algorithm
Ali Dadras, Sourasekhar Banerjee, Karthik Prakhya +1
Federated learning (FL) has gained a lot of attention in recent years for building privacy-preserving collaborative learning systems. However, FL algorithms for constrained machine…
Personalized Multi-tier Federated Learning
Sourasekhar Banerjee, Ali Dadras, Alp Yurtsever +1
The key challenge of personalized federated learning (PerFL) is to capture the statistical heterogeneity properties of data with inexpensive communications and gain customized perf…