Federated Learning

(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning NeurIPS 2024 FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion NeurIPS 2024 Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning NeurIPS 2024 Hierarchical Federated Learning with Multi-Timescale Gradient Correction NeurIPS 2024 HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning NeurIPS 2024 On the Convergence of Hierarchical Federated Learning with Partial Worker Participation UAI 2024 On the Role of Server Momentum in Federated Learning AAAI 2024 Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images AAAI 2024 Promoting Data and Model Privacy in Federated Learning through Quantized LoRA EMNLP 2024 Revisiting Ensembling in One-Shot Federated Learning NeurIPS 2024 Safely Learning with Private Data: A Federated Learning Framework for Large Language Model EMNLP 2024 Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradients EMNLP 2024 SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead NeurIPS 2024 Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains NeurIPS 2024 Text-Enhanced Data-free Approach for Federated Class-Incremental Learning CVPR 2024 Thinking Forward: Memory-Efficient Federated Finetuning of Language Models NeurIPS 2024 Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting NeurIPS 2024 User Inference Attacks on Large Language Models EMNLP 2024 z-SignFedAvg: A Unified Stochastic Sign-Based Compression for Federated Learning AAAI 2024 Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices NeurIPS 2023 Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework AAAI 2023 CaPriDe Learning: Confidential and Private Decentralized Learning Based on Encryption-Friendly Distillation Loss CVPR 2023 Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence ICCV 2023 Complement Sparsification: Low-Overhead Model Pruning for Federated Learning AAAI 2023 Efficient Federated Domain Translation ICLR 2023