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Federated Learning

1355 directly classified papers

Papers per year

Papers

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings NIPS 2022
CalFAT: Calibrated Federated Adversarial Training with Label Skewness NIPS 2022
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training ICML 2022
On Divergence Measures for Bayesian Pseudocoresets NIPS 2022
Resource-Adaptive Federated Learning with All-In-One Neural Composition NIPS 2022
Fairness in Federated Learning via Core-Stability NIPS 2022
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing NIPS 2022
Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees NIPS 2022
Architecture Agnostic Federated Learning for Neural Networks ICML 2022
Personalized Federated Learning through Local Memorization ICML 2022
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification ICML 2022
QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning ICML 2022
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization ICML 2022
Neural Tangent Kernel Empowered Federated Learning ICML 2022
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks NIPS 2022
Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework NIPS 2022
Resilient and Communication Efficient Learning for Heterogeneous Federated Systems ICML 2022
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning IJCAI 2022
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training NIPS 2022
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach NIPS 2022
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server IJCAI 2022
Federated Submodel Optimization for Hot and Cold Data Features NIPS 2022
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling ICML 2022
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects NIPS 2022
Data-splitting improves statistical performance in overparameterized regimes AISTATS 2022