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Federated Learning

1355 directly classified papers

Papers per year

Papers

Mind the Cost of Scaffold! Benign Clients May Even Become Accomplices of Backdoor Attack ICCV 2025
pFedES: Generalized Proxy Feature Extractor Sharing for Model Heterogeneous Personalized Federated Learning AAAI 2025
Privacy-Preserving V2X Collaborative Perception Integrating Unknown Collaborators AAAI 2025
Dual-calibrated Co-training Framework for Personalized Federated Semi-Supervised Medical Image Segmentation AAAI 2025
Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation ICCV 2025
Federated Weakly Supervised Video Anomaly Detection with Multimodal Prompt AAAI 2025
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning AAAI 2025
Efficient Federated Learning via Clients-to-Server Knowledge Distillation (Student Abstract) AAAI 2025
Population Normalization for Federated Learning CVPR 2025
Pilot: Building the Federated Multimodal Instruction Tuning Framework AAAI 2025
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond JMLR 2025
FedFSL-CFRD: Personalized Federated Few-Shot Learning with Collaborative Feature Representation Disentanglement AAAI 2025
On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients AISTATS 2025
Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities WACV 2025
Improving Federated Domain Generalization Through Dynamical Weights Calculated from Data Influences on Global Model Update AAAI 2025
FedCoT: Federated Chain-of-Thought Distillation for Large Language Models EMNLP 2025
MobileA3gent: Training Mobile GUI Agents Using Decentralized Self-Sourced Data from Diverse Users EMNLP 2025
Label Leakage in Vertical Federated Learning: A Survey IJCAI 2024
From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching IJCAI 2024
FedPFT: Federated Proxy Fine-Tuning of Foundation Models IJCAI 2024
FedFa: A Fully Asynchronous Training Paradigm for Federated Learning IJCAI 2024
Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection IJCAI 2024
Scalable Federated Unlearning via Isolated and Coded Sharding IJCAI 2024
FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding? NIPS 2024
Unlearning during Learning: An Efficient Federated Machine Unlearning Method IJCAI 2024