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Federated Learning

497 directly classified papers

Papers per year

Papers

Federated Ensemble-Directed Offline Reinforcement Learning NIPS 2024
Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts AAAI 2024
Cross-Feature Contrastive Loss for Decentralized Deep Learning on Heterogeneous Data WACV 2024
Provably Convergent Federated Trilevel Learning AAAI 2024
TransFed: A Way To Epitomize Focal Modulation Using Transformer-Based Federated Learning WACV 2024
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing AAAI 2024
Communication-Efficient Federated Learning With Data and Client Heterogeneity AISTATS 2024
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning AAAI 2024
Invariant Aggregator for Defending against Federated Backdoor Attacks AISTATS 2024
FedMut: Generalized Federated Learning via Stochastic Mutation AAAI 2024
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization AISTATS 2024
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates AISTATS 2024
Foreseeing Reconstruction Quality of Gradient Inversion: An Optimization Perspective AAAI 2024
Hierarchical Federated Learning with Multi-Timescale Gradient Correction NIPS 2024
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space AAAI 2024
Federated Experiment Design under Distributed Differential Privacy AISTATS 2024
Federated Label-Noise Learning with Local Diversity Product Regularization AAAI 2024
Adaptive Compression in Federated Learning via Side Information AISTATS 2024
FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning AAAI 2024
A Survey on Efficient Federated Learning Methods for Foundation Model Training IJCAI 2024
Exploiting Label Skews in Federated Learning with Model Concatenation AAAI 2024
Intelligent Agents for Auction-based Federated Learning: A Survey IJCAI 2024
FedGCR: Achieving Performance and Fairness for Federated Learning with Distinct Client Types via Group Customization and Reweighting AAAI 2024
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data NIPS 2024
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity AAAI 2024