Learning Paradigms

EM for Mixture of Linear Regression with Clustered Data AISTATS 2024 EMGAN: Early-Mix-GAN on Extracting Server-Side Model in Split Federated Learning AAAI 2024 Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity NeurIPS 2024 Enhancing Alignment using Curriculum Learning & Ranked Preferences EMNLP 2024 Enhancing Byzantine-Resistant Aggregations with Client Embedding EMNLP 2024 Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression AISTATS 2024 Exploiting Label Skews in Federated Learning with Model Concatenation AAAI 2024 Exploring One-Shot Semi-supervised Federated Learning with Pre-trained Diffusion Models AAAI 2024 FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding? NeurIPS 2024 Fair and Efficient Contribution Valuation for Vertical Federated Learning ICLR 2024 Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity CVPR 2024 Fairness and Privacy Guarantees in Federated Contextual Bandits ACML 2024 Fairness-Aware Meta-Learning via Nash Bargaining NeurIPS 2024 FairTrade: Achieving Pareto-Optimal Trade-Offs between Balanced Accuracy and Fairness in Federated Learning AAAI 2024 FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated Learning IJCAI 2024 Feature Norm Regularized Federated Learning: Utilizing Data Disparities for Model Performance Gains IJCAI 2024 FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise AAAI 2024 FedAS: Bridging Inconsistency in Personalized Federated Learning CVPR 2024 FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update AAAI 2024 FedAST: Federated Asynchronous Simultaneous Training UAI 2024 FedAvP: Augment Local Data via Shared Policy in Federated Learning NeurIPS 2024 FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization JMLR 2024 FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants AAAI 2024 FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning AAAI 2024 FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning AAAI 2024