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Federated Learning

551 directly classified papers

Papers per year

Papers

Federated Binary Matrix Factorization Using Proximal Optimization AAAI 2025
Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning ICCV 2025
FedCPD:Personalized Federated Learning with Prototype-Enhanced Representation and Memory Distillation IJCAI 2025
MultiSFL: Towards Accurate Split Federated Learning via Multi-Model Aggregation and Knowledge Replay AAAI 2025
MFL-Owner: Ownership Protection for Multi-modal Federated Learning via Orthogonal Transform Watermark AAAI 2025
Overcoming Heterogeneous Data in Federated Medical Vision-Language Pre-training: A Triple-Embedding Model Selector Approach AAAI 2025
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach AAAI 2025
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning AAAI 2024
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators AAAI 2024
On Disentanglement of Asymmetrical Knowledge Transfer for Modality-Task Agnostic Federated Learning AAAI 2024
Formal Logic Enabled Personalized Federated Learning through Property Inference AAAI 2024
Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning against Attribute Inference Attacks AAAI 2024
Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models AAAI 2024
Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity NIPS 2024
No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation AAAI 2024
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity AAAI 2024
FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization JMLR 2024
Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning NAACL 2024
Federated Linear Contextual Bandits with Heterogeneous Clients AISTATS 2024
Personalized Federated Learning for Cross-City Traffic Prediction IJCAI 2024
FedGCR: Achieving Performance and Fairness for Federated Learning with Distinct Client Types via Group Customization and Reweighting AAAI 2024
Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning WACV 2024
Practical Hybrid Gradient Compression for Federated Learning Systems IJCAI 2024
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting NIPS 2024
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning NIPS 2024