39 citations · 88 across the 9 of their papers we have counts for
10 papers
CoBo: Collaborative Learning via Bilevel Optimization
Diba Hashemi, Lie He, Martin Jaggi
Collaborative learning is an important tool to train multiple clients more effectively by enabling communication among clients. Identifying helpful clients, however, presents chall…
Provably Personalized and Robust Federated Learning
Mariel Werner, Lie He, Michael Jordan +2
Identifying clients with similar objectives and learning a model-per-cluster is an intuitive and interpretable approach to personalization in federated learning. However, doing so…
Debiasing Conditional Stochastic Optimization
Lie He, Shiva Prasad Kasiviswanathan
In this paper, we study the conditional stochastic optimization (CSO) problem which covers a variety of applications including portfolio selection, reinforcement learning, robust l…
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…
RelaySum for Decentralized Deep Learning on Heterogeneous Data
Thijs Vogels, Lie He, Anastasia Koloskova +4
In decentralized machine learning, workers compute model updates on their local data. Because the workers only communicate with few neighbors without central coordination, these up…
Learning from History for Byzantine Robust Optimization
Sai Praneeth Karimireddy, Lie He, Martin Jaggi
Byzantine robustness has received significant attention recently given its importance for distributed and federated learning. In spite of this, we identify severe flaws in existing…