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Federated Learning

551 directly classified papers

Papers per year

Papers

Communication-Efficient Federated Learning With Data and Client Heterogeneity AISTATS 2024
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression AISTATS 2024
Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline CVPR 2024
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning AAAI 2024
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models EMNLP 2024
EM for Mixture of Linear Regression with Clustered Data AISTATS 2024
Federated Adaptation for Foundation Model-based Recommendations IJCAI 2024
Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks AISTATS 2024
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning IJCAI 2024
Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters AISTATS 2024
FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated Learning IJCAI 2024
From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching IJCAI 2024
Adaptive Compression in Federated Learning via Side Information AISTATS 2024
Mixing Gradients in Neural Networks as a Strategy To Enhance Privacy in Federated Learning WACV 2024
Redefining Contributions: Shapley-Driven Federated Learning IJCAI 2024
FedES: Federated Early-Stopping for Hindering Memorizing Heterogeneous Label Noise IJCAI 2024
FedPFT: Federated Proxy Fine-Tuning of Foundation Models IJCAI 2024
Stakeholder-oriented Decision Support for Auction-based Federated Learning IJCAI 2024
LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game IJCAI 2024
Scalable Federated Unlearning via Isolated and Coded Sharding IJCAI 2024
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning IJCAI 2024
Feature Norm Regularized Federated Learning: Utilizing Data Disparities for Model Performance Gains IJCAI 2024
FedGCR: Achieving Performance and Fairness for Federated Learning with Distinct Client Types via Group Customization and Reweighting AAAI 2024
Towards Fair Graph Federated Learning via Incentive Mechanisms AAAI 2024
Private and Personalized Frequency Estimation in a Federated Setting NIPS 2024