federated learning

FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning EMNLP 2023 FeDXL: Provable Federated Learning for Deep X-Risk Optimization ICML 2023 Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization NeurIPS 2023 FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification ICLR 2023 FLASH: Automating federated learning using CASH UAI 2023 Flash: Concept Drift Adaptation in Federated Learning ICML 2023 FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning ICLR 2023 Flow: Per-instance Personalized Federated Learning NeurIPS 2023 FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout NeurIPS 2023 FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation ICCV 2023 From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning ICML 2023 FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation ICCV 2023 Game-theoretic Mechanisms for Eliciting Accurate Information IJCAI 2023 Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses ICLR 2023 Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels JMLR 2023 Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning ICCV 2023 GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization ICCV 2023 Global Balanced Experts for Federated Long-Tailed Learning ICCV 2023 Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization NeurIPS 2023 Globally Consistent Federated Graph Autoencoder for Non-IID Graphs IJCAI 2023 Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data NeurIPS 2023 Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy NeurIPS 2023 GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated Catastrophic Forgetting CVPR 2023 Guiding The Last Layer in Federated Learning with Pre-Trained Models NeurIPS 2023 Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition NeurIPS 2023