conftrace_
← Learning Types
Deep Learning › Learning Types ›

Federated Learning

169 papers

Papers per year

1
2
4
16
31
27
37
47
4

Papers

FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction NIPS 2022
Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching NIPS 2022
FLAIR: Federated Learning Annotated Image Repository NIPS 2022
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images AAAI 2022
FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition AAAI 2022
Federated Learning for Face Recognition with Gradient Correction AAAI 2022
SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures AAAI 2022
Implicit Gradient Alignment in Distributed and Federated Learning AAAI 2022
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data AAAI 2022
DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems AAAI 2022
FedProto: Federated Prototype Learning across Heterogeneous Clients AAAI 2022
Contribution-Aware Federated Learning for Smart Healthcare AAAI 2022
Class-Wise Adaptive Self Distillation for Federated Learning on Non-IID Data (Student Abstract) AAAI 2022
AsyncFL: Asynchronous Federated Learning Using Majority Voting with Quantized Model Updates (Student Abstract) AAAI 2022
FedCC: Federated Learning with Consensus Confirmation for Byzantine Attack Resistance (Student Abstract) AAAI 2022
Scaling Language Model Size in Cross-Device Federated Learning ACL 2022
Intrinsic Gradient Compression for Scalable and Efficient Federated Learning ACL 2022
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning CVPR 2022
RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning CVPR 2022
Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation EMNLP 2022
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale ICML 2022
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout NIPS 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients NIPS 2021
Differentially Private Learning with Adaptive Clipping NIPS 2021
On Large-Cohort Training for Federated Learning NIPS 2021