6 papers · 1 filter
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…
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…
Secure Byzantine-Robust Machine Learning
Lie He, Sai Praneeth Karimireddy, Martin Jaggi
Increasingly machine learning systems are being deployed to edge servers and devices (e.g. mobile phones) and trained in a collaborative manner. Such distributed/federated/decentra…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…