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Found 11 papers, 8 papers with code
CoBo: Collaborative Learning via Bilevel Optimization
Collaborative learning is an important tool to train multiple clients more effectively by enabling communication among clients.
Provably Personalized and Robust Federated Learning
Identifying clients with similar objectives and learning a model-per-cluster is an intuitive and interpretable approach to personalization in federated learning.
Debiasing Conditional Stochastic Optimization
In this paper, we study the conditional stochastic optimization (CSO) problem which covers a variety of applications including portfolio selection, reinforcement learning, robust learning, causal inference, etc.
Byzantine-Robust Decentralized Learning via ClippedGossip
In this paper, we study the challenging task of Byzantine-robust decentralized training on arbitrary communication graphs.
RelaySum for Decentralized Deep Learning on Heterogeneous Data
A key challenge, primarily in decentralized deep learning, remains the handling of differences between the workers' local data distributions.
Learning from History for Byzantine Robust Optimization
Secondly, we prove that even if the aggregation rules may succeed in limiting the influence of the attackers in a single round, the attackers can couple their attacks across time eventually leading to divergence.
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling
In Byzantine-robust distributed optimization, a central server wants to train a machine learning model over data distributed across multiple workers.
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
In Byzantine robust distributed or federated learning, a central server wants to train a machine learning model over data distributed across multiple workers.
Secure Byzantine-Robust Machine Learning
Increasingly machine learning systems are being deployed to edge servers and devices (e. g. mobile phones) and trained in a collaborative manner.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
COLA: Decentralized Linear Learning
Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy.
