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Federated Learning

497 directly classified papers

Papers per year

Papers

Byzantine-Robust Decentralized Learning via Remove-then-Clip Aggregation AAAI 2024
Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models AAAI 2024
Compressed and distributed least-squares regression: convergence rates with applications to federated learning JMLR 2024
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources NIPS 2024
Open-Vocabulary Federated Learning with Multimodal Prototyping NAACL 2024
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates AISTATS 2024
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization AISTATS 2024
Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers AAAI 2024
PerFedRLNAS: One-for-All Personalized Federated Neural Architecture Search AAAI 2024
Federated Experiment Design under Distributed Differential Privacy AISTATS 2024
BOBA: Byzantine-Robust Federated Learning with Label Skewness AISTATS 2024
Exploring One-Shot Semi-supervised Federated Learning with Pre-trained Diffusion Models AAAI 2024
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning AAAI 2024
Compression with Exact Error Distribution for Federated Learning AISTATS 2024
Invariant Aggregator for Defending against Federated Backdoor Attacks AISTATS 2024
Communication-Efficient Federated Learning With Data and Client Heterogeneity AISTATS 2024
Adaptive Compression in Federated Learning via Side Information AISTATS 2024
Federated Causality Learning with Explainable Adaptive Optimization AAAI 2024
Chronic Poisoning: Backdoor Attack against Split Learning AAAI 2024
On Disentanglement of Asymmetrical Knowledge Transfer for Modality-Task Agnostic Federated Learning AAAI 2024
Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization AAAI 2024
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators AAAI 2024
A Huber Loss Minimization Approach to Byzantine Robust Federated Learning AAAI 2024
Mixing Gradients in Neural Networks as a Strategy To Enhance Privacy in Federated Learning WACV 2024
DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations AAAI 2024