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Federated Learning

1355 directly classified papers

Papers per year

Papers

Federated Domain Adaptation for ASR with Full Self-Supervision INTERSPEECH 2022
Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning CVPR 2022
On Sample Optimality in Personalized Collaborative and Federated Learning NIPS 2022
VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely? NIPS 2022
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression NIPS 2022
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning NIPS 2022
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise NIPS 2022
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams NIPS 2022
On Privacy and Personalization in Cross-Silo Federated Learning NIPS 2022
A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning NIPS 2022
SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning NIPS 2022
LAMP: Extracting Text from Gradients with Language Model Priors NIPS 2022
FedPop: A Bayesian Approach for Personalised Federated Learning NIPS 2022
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning NIPS 2022
Near-Optimal Private and Scalable $k$-Clustering NIPS 2022
On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond NIPS 2022
Distributed Learning of Conditional Quantiles in the Reproducing Kernel Hilbert Space NIPS 2022
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations NIPS 2022
Byzantine-tolerant federated Gaussian process regression for streaming data NIPS 2022
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning NIPS 2022
Private and Communication-Efficient Algorithms for Entropy Estimation NIPS 2022
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning NIPS 2022
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization NIPS 2022
A Unified Analysis of Federated Learning with Arbitrary Client Participation NIPS 2022
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning NIPS 2022