One-shot Empirical Privacy Estimation for Federated Learning.
Benchmarking Algorithms for Federated Domain Generalization.
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging.
A Mutual Information Perspective on Federated Contrastive Learning.
Stochastic Controlled Averaging for Federated Learning with Communication Compression.
Bayesian Coreset Optimization for Personalized Federated Learning.
Fair and Efficient Contribution Valuation for Vertical Federated Learning.
FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization.
Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning.
Federated Q-Learning: Linear Regret Speedup with Low Communication Cost.
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning.
Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients.
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation.
Communication-Efficient Federated Non-Linear Bandit Optimization.
Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration.
Adaptive Federated Learning with Auto-Tuned Clients.
Improving LoRA in Privacy-preserving Federated Learning.
Incentivized Truthful Communication for Federated Bandits.
Federated Wasserstein Distance.
Incentive-Aware Federated Learning with Training-Time Model Rewards.
An improved analysis of per-sample and per-update clipping in federated learning.
PeFLL: Personalized Federated Learning by Learning to Learn.
Robust Training of Federated Models with Extremely Label Deficiency.
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit.
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data.
FedImpro: Measuring and Improving Client Update in Federated Learning.
FedWon: Triumphing Multi-domain Federated Learning Without Normalization.
Federated Recommendation with Additive Personalization.
On Differentially Private Federated Linear Contextual Bandits.
Federated Text-driven Prompt Generation for Vision-Language Models.
Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning.
Federated Causal Discovery from Heterogeneous Data.
FedInverse: Evaluating Privacy Leakage in Federated Learning.
FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning.
FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation.
Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate.
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers.
Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition.
FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent.
Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting.
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler.
Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages.
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting.
Accurate Forgetting for Heterogeneous Federated Continual Learning.
VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks.
Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning.
VFLAIR: A Research Library and Benchmark for Vertical Federated Learning.
Momentum Benefits Non-iid Federated Learning Simply and Provably.
Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory.
Effective and Efficient Federated Tree Learning on Hybrid Data.
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity.
Personalized Federated Learning with Feature Alignment and Classifier Collaboration.
MocoSFL: enabling cross-client collaborative self-supervised learning.
Single-shot General Hyper-parameter Optimization for Federated Learning.
Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated.
FedExP: Speeding up Federated Averaging via Extrapolation.
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection.
DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity.
Machine Unlearning of Federated Clusters.
Federated Neural Bandits.
FedFA: Federated Feature Augmentation.
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach.
Better Generative Replay for Continual Federated Learning.
Federated Learning from Small Datasets.
Federated Nearest Neighbor Machine Translation.
Test-Time Robust Personalization for Federated Learning.
DepthFL : Depthwise Federated Learning for Heterogeneous Clients.
Towards Addressing Label Skews in One-Shot Federated Learning.
Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning.
Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation.
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication.
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses.
Effective passive membership inference attacks in federated learning against overparameterized models.
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification.
Multimodal Federated Learning via Contrastive Representation Ensemble.
Faster federated optimization under second-order similarity.
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy.
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation.
PerFedMask: Personalized Federated Learning with Optimized Masking Vectors.
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data.
FedDAR: Federated Domain-Aware Representation Learning.
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning.
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning.
Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses.
Efficient Federated Domain Translation.
On the Importance and Applicability of Pre-Training for Federated Learning.
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models.
A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy.
Instance-wise Batch Label Restoration via Gradients in Federated Learning.
Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity.
Meta Knowledge Condensation for Federated Learning.
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning.
Sparse Random Networks for Communication-Efficient Federated Learning.
Combating Exacerbated Heterogeneity for Robust Decentralized Models.
Hyperparameter Optimization through Neural Network Partitioning.
Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision?
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top.
Last updated 4 months ago