NeurlPS

2024

• FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection.

• Federated Ensemble-Directed Offline Reinforcement Learning.

• Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis.

• Resource-Aware Federated Self-Supervised Learning with Global Class Representations.

• DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices.

• SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning.

• Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources.

• FedAvP: Augment Local Data via Shared Policy in Federated Learning.

• Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning.

• FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations.

• Communication-Efficient Federated Group Distributionally Robust Optimization.

• Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity.

• FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion.

• Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method.

• FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference.

• Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views.

• FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning.

• Dual-Personalizing Adapter for Federated Foundation Models.

• The Sample-Communication Complexity Trade-off in Federated Q-Learning.

• pfl-research: simulation framework for accelerating research in Private Federated Learning.

• (FL)2: Overcoming Few Labels in Federated Semi-Supervised Learning.

• Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data.

• Private and Personalized Frequency Estimation in a Federated Setting.

• Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors.

• FedGMark: Certifiably Robust Watermarking for Federated Graph Learning.

• HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning.

• pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning.

• Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning.

• Federated Graph Learning for Cross-Domain Recommendation.

• Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning.

• Federated Model Heterogeneous Matryoshka Representation Learning.

• Revisiting Ensembling in One-Shot Federated Learning.

• Thinking Forward: Memory-Efficient Federated Finetuning of Language Models.

• FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?

• Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning.

• Personalized Federated Learning via Feature Distribution Adaptation.

• Hierarchical Federated Learning with Multi-Timescale Gradient Correction.

• Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift.

• FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation.

• Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data.

• Federated Learning over Connected Modes.

• A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs.

• DoFIT: Domain-aware Federated Instruction Tuning with Alleviated Catastrophic Forgetting.

• SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead.

• Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains.

• Low Precision Local Training is Enough for Federated Learning.

• Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning.

• Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting.

• Stabilized Proximal-Point Methods for Federated Optimization.

• Why Go Full? Elevating Federated Learning Through Partial Network Updates.

• Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients.

• A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings.

• One-shot Federated Learning via Synthetic Distiller-Distillate Communication.

• Convergence Analysis of Split Federated Learning on Heterogeneous Data.

• Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability.

• RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation.

• SPEAR: Exact Gradient Inversion of Batches in Federated Learning.

• Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data.

• FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models.

• Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning.

• Federated Black-Box Adaptation for Semantic Segmentation.

• FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction.

• FedGMKD: An Efficient Prototype Federated Learning Framework through Knowledge Distillation and Discrepancy-Aware Aggregation.

• Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning.

• Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning.

• The Power of Extrapolation in Federated Learning.

• Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups.

• Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration.

• DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans.

• FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection.

• FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction.

• Improving Generalization in Federated Learning with Model-Data Mutual Information Regularization: A Posterior Inference Approach.

• Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data.

• A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm.

2023

• Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking.

• Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems.

• Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection.

• Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization.

• Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning.

• Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition.

• Federated Conditional Stochastic Optimization.

• SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning.

• Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds.

• RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks.

• Spectral Co-Distillation for Personalized Federated Learning.

• Federated Compositional Deep AUC Maximization.

• Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training.

• Federated Learning via Meta-Variational Dropout.

• Solving a Class of Non-Convex Minimax Optimization in Federated Learning.

• A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning.

• Eliminating Domain Bias for Federated Learning in Representation Space.

• FedL2P: Federated Learning to Personalize.

• One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning.

• Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization.

• DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning.

• Flow: Per-instance Personalized Federated Learning.

• Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning.

• Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction.

• Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning.

• PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning.

• Private Federated Frequency Estimation: Adapting to the Hardness of the Instance.

• Towards Personalized Federated Learning via Heterogeneous Model Reassembly.

• Federated Learning with Bilateral Curation for Partially Class-Disjoint Data.

• Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning.

• SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning.

• Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM.

• Federated Multi-Objective Learning.

• Structured Federated Learning through Clustered Additive Modeling.

• DELTA: Diverse Client Sampling for Fasting Federated Learning.

• FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning.

• Incentivized Communication for Federated Bandits.

• Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization.

• Federated Learning with Manifold Regularization and Normalized Update Reaggregation.

• Convergence Analysis of Sequential Federated Learning on Heterogeneous Data.

• Federated Spectral Clustering via Secure Similarity Reconstruction.

• Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates.

• FedFed: Feature Distillation against Data Heterogeneity in Federated Learning.

• A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning.

• Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense.

• EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning.

• Federated Linear Bandits with Finite Adversarial Actions.

• IBA: Towards Irreversible Backdoor Attacks in Federated Learning.

• A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks.

• StableFDG: Style and Attention Based Learning for Federated Domain Generalization.

• Guiding The Last Layer in Federated Learning with Pre-Trained Models.

• Multiply Robust Federated Estimation of Targeted Average Treatment Effects.

• Dynamic Personalized Federated Learning with Adaptive Differential Privacy.

• FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout.

• FedNAR: Federated Optimization with Normalized Annealing Regularization.

• Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer.

• Adaptive Test-Time Personalization for Federated Learning.

• FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks.

• Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization.

2022

• Federated Submodel Optimization for Hot and Cold Data Features.

• On Sample Optimality in Personalized Collaborative and Federated Learning.

• VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?

• CalFAT: Calibrated Federated Adversarial Training with Label Skewness.

• Resource-Adaptive Federated Learning with All-In-One Neural Composition.

• SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression.

• A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits.

• FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings.

• Fairness in Federated Learning via Core-Stability.

• On Privacy and Personalization in Cross-Silo Federated Learning.

• A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning.

• SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning.

• FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning.

• Recovering Private Text in Federated Learning of Language Models.

• FedPop: A Bayesian Approach for Personalised Federated Learning.

• pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning.

• DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing.

• Byzantine-tolerant federated Gaussian process regression for streaming data.

• Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning.

• Taming Fat-Tailed ("Heavier-Tailed" with Potentially Infinite Variance) Noise in Federated Learning.

• Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning.

• SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training.

• A Unified Analysis of Federated Learning with Arbitrary Client Participation.

• Federated Learning from Pre-Trained Models: A Contrastive Learning Approach.

• Self-Aware Personalized Federated Learning.

• A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning.

• DENSE: Data-Free One-Shot Federated Learning.

• An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects.

• Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering.

• FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction.

• Global Convergence of Federated Learning for Mixed Regression.

• Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness.

• TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels.

• Personalized Online Federated Learning with Multiple Kernels.

• Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework.

• Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching.

• SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning.

• FLAIR: Federated Learning Annotated Image Repository.

• Communication Efficient Federated Learning for Generalized Linear Bandits.

• Preservation of the Global Knowledge by Not-True Distillation in Federated Learning.

• FedSR: A Simple and Effective Domain Generalization Method for Federated Learning.