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Federated Learning

497 directly classified papers

Papers per year

Papers

Robust and Actively Secure Serverless Collaborative Learning NIPS 2023
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting NIPS 2023
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM NIPS 2023
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices NIPS 2023
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning NIPS 2023
Bias-Eliminating Augmentation Learning for Debiased Federated Learning CVPR 2023
DynaFed: Tackling Client Data Heterogeneity With Global Dynamics CVPR 2023
How To Prevent the Poor Performance Clients for Personalized Federated Learning? CVPR 2023
Tunable Soft Prompts are Messengers in Federated Learning EMNLP 2023
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond ICML 2023
Elastic Aggregation for Federated Optimization CVPR 2023
On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data CVPR 2023
Private Federated Learning with Autotuned Compression ICML 2023
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation CVPR 2023
Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack CVPR 2023
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape ICML 2023
FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks ICML 2023
On Biased Compression for Distributed Learning JMLR 2023
Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets ACL 2023
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data NIPS 2023
Efficient On-Device Training via Gradient Filtering CVPR 2023
Multi-Agent Best Arm Identification with Private Communications ICML 2023
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout AISTATS 2023
Reconstructing Training Data from Model Gradient, Provably AISTATS 2023
Differentially Private Matrix Completion through Low-rank Matrix Factorization AISTATS 2023