federated learning
A distributed machine learning approach that allows multiple devices to collaboratively learn a shared model while keeping their data locally, enhancing privacy and reducing latency.
- A Fair Federated Learning Method for Handling Client Participation Probability Inconsistencies in Heterogeneous Environments
- A Unified Analysis of Stochastic Gradient Descent with Arbitrary Data Permutations and Beyond
- Adaptive Latent-Space Constraints in Personalized Federated Learning
- Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning
- Bi-Directional Communication-Efficient Stochastic FL via Remote Source Generation
- Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning
- DKDR: Dynamic Knowledge Distillation for Reliability in Federated Learning
- DictPFL: Efficient and Private Federated Learning on Encrypted Gradients
- Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix
- Diffusion Federated Dataset
- Efficient Adaptive Federated Optimization
- Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
- Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers, and Gradient Clipping
- Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable
- FLUX: Efficient Descriptor-Driven Clustered Federated Learning under Arbitrary Distribution Shifts
- FLiP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning
- FORLA: Federated Object-centric Representation Learning with Slot Attention
- FedEL: Federated Elastic Learning for Heterogeneous Devices
- FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
- FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
- FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning
- FedRACE: A Hierarchical and Statistical Framework for Robust Federated Learning
- FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
- FedRW: Efficient Privacy-Preserving Data Reweighting for Enhancing Federated Learning of Language Models
- FedSVD: Adaptive Orthogonalization for Private Federated Learning with LoRA
- FedWMSAM: Fast and Flat Federated Learning via Weighted Momentum and Sharpness-Aware Minimization
- Federated Dialogue-Semantic Diffusion for Emotion Recognition under Incomplete Modalities
- Flick: Empowering Federated Learning with Commonsense Knowledge
- FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models
- Gains: Fine-grained Federated Domain Adaptation in Open Set
- LLM at Network Edge: A Layer-wise Efficient Federated Fine-tuning Approach
- Layer-wise Update Aggregation with Recycling for Communication-Efficient Federated Learning
- Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings
- MARS: A Malignity-Aware Backdoor Defense in Federated Learning
- Multiplayer Federated Learning: Reaching Equilibrium with Less Communication
- NormFit: A Lightweight Solution for Few-Shot Federated Learning with Non-IID Data
- Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections
- Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning
- Rethinking Fair Federated Learning from Parameter and Client View
- Rising from Ashes: Generalized Federated Learning via Dynamic Parameter Reset
- Robust Estimation Under Heterogeneous Corruption Rates
- SPFL: Sequential updates with Parallel aggregation for Enhanced Federated Learning under Category and Domain Shifts
- Sharp Gaussian approximations for Decentralized Federated Learning
- Sketched Gaussian Mechanism for Private Federated Learning
- Soft-consensual Federated Learning for Data Heterogeneity via Multiple Paths
- Streaming Federated Learning with Markovian Data
- Subgraph Federated Learning via Spectral Methods
- Tabula: A Tabular Self-Supervised Foundation Model for Single-Cell Transcriptomics
- Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
- Tight Bounds for Maximum Weight Matroid Independent Set and Matching in the Zero Communication Model
- Tight High-Probability Bounds for Nonconvex Heavy-Tailed Scenario under Weaker Assumptions
- Towards Robust Parameter-Efficient Fine-Tuning for Federated Learning
- Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning
- You Only Communicate Once: One-shot Federated Low-Rank Adaptation of MLLM