SOTAVerified

Federated Learning

Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.

This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.

Papers

Showing 43514400 of 6771 papers

TitleStatusHype
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation0
FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis0
FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings0
FedPDC:Federated Learning for Public Dataset Correction0
FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation0
FedPD: Federated Open Set Recognition with Parameter Disentanglement0
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing0
FedPerm: Private and Robust Federated Learning by Parameter Permutation0
FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning0
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning0
FedPID: An Aggregation Method for Federated Learning0
FedPIDAvg: A PID controller inspired aggregation method for Federated Learning0
Fed-pilot: Optimizing LoRA Allocation for Efficient Federated Fine-Tuning with Heterogeneous Clients0
FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning0
FedPNN: One-shot Federated Classification via Evolving Clustering Method and Probabilistic Neural Network hybrid0
FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence0
FedPop: A Bayesian Approach for Personalised Federated Learning0
FedProf: Selective Federated Learning with Representation Profiling0
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer0
FedProphet: Memory-Efficient Federated Adversarial Training via Theoretic-Robustness and Low-Inconsistency Cascade Learning0
FedPrune: Towards Inclusive Federated Learning0
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis0
FedPT: Federated Proxy-Tuning of Large Language Models on Resource-Constrained Edge Devices0
FedQNN: Federated Learning using Quantum Neural Networks0
FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation0
FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher0
FedRAD: Federated Robust Adaptive Distillation0
FedRand: Enhancing Privacy in Federated Learning with Randomized LoRA Subparameter Updates0
FedRBE -- a decentralized privacy-preserving federated batch effect correction tool for omics data based on limma0
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning0
FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission0
FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems0
FedRecon: Missing Modality Reconstruction in Distributed Heterogeneous Environments0
FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information0
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning0
FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning0
FedRepOpt: Gradient Re-parametrized Optimizers in Federated Learning0
FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers0
FedRE: Robust and Effective Federated Learning with Privacy Preference0
FedReview: A Review Mechanism for Rejecting Poisoned Updates in Federated Learning0
FedRewind: Rewinding Continual Model Exchange for Decentralized Federated Learning0
FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality0
FedRight: An Effective Model Copyright Protection for Federated Learning0
FedRobo: Federated Learning Driven Autonomous Inter Robots Communication For Optimal Chemical Sprays0
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA0
FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing0
FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models0
FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing0
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning0
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75Unverified
2SiloBN + SAMmIoU49.1Unverified
3SiloBNmIoU45.96Unverified
4FedSAM + SWAmIoU43.42Unverified
5FedASAM + SWAmIoU43.02Unverified
6FedAvg + SWAmIoU42.48Unverified
7FedASAMmIoU42.27Unverified
8FedSAMmIoU41.22Unverified
9FedAvgmIoU38.65Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32Unverified
2FedSAM + SWAAcc@1-1262Clients68.12Unverified
3FedAvg + SWAAcc@1-1262Clients67.52Unverified
4FedASAMAcc@1-1262Clients64.23Unverified
5FedSAMAcc@1-1262Clients63.72Unverified
6FedAvgAcc@1-1262Clients61.91Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64Unverified
2FedASAMACC@1-100Clients39.76Unverified
3FedSAM + SWAACC@1-100Clients39.51Unverified
4FedSAMACC@1-100Clients36.93Unverified
5FedAvgACC@1-100Clients36.74Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62Unverified
2FedASAMACC@1-100Clients40.81Unverified
3FedSAM + SWAACC@1-100Clients39.24Unverified
4FedAvgACC@1-100Clients38.59Unverified
5FedSAMACC@1-100Clients38.56Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72Unverified
2FedSAM + SWAACC@1-100Clients46.76Unverified
3FedASAMACC@1-100Clients46.58Unverified
4FedSAMACC@1-100Clients44.84Unverified
5FedAvgACC@1-100Clients41.27Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27Unverified
2FedASAMACC@1-100Clients47.78Unverified
3FedSAM + SWAACC@1-100Clients46.47Unverified
4FedSAMACC@1-100Clients46.05Unverified
5FedAvgACC@1-100Clients42.17Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17Unverified
2FedSAM + SWAACC@1-100Clients47.96Unverified
3FedASAMACC@1-100Clients45.61Unverified
4FedSAMACC@1-100Clients44.73Unverified
5FedAvgACC@1-100Clients40.43Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01Unverified
2FedSAM + SWAACC@1-100Clients39.3Unverified
3FedASAMACC@1-100Clients36.04Unverified
4FedSAMACC@1-100Clients31.04Unverified
5FedAvgACC@1-100Clients30.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97Unverified
2FedASAM + SWAACC@1-100Clients54.79Unverified
3FedSAM + SWAACC@1-100Clients53.67Unverified
4FedSAMACC@1-100Clients53.39Unverified
5FedAvgACC@1-100Clients50.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5Unverified
2FedSAM + SWAACC@1-100Clients54.36Unverified
3FedASAM + SWAACC@1-100Clients54.1Unverified
4FedSAMACC@1-100Clients53.97Unverified
5FedAvgACC@1-100Clients50.66Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81Unverified
2FedSAMACC@1-100Clients54.01Unverified
3FedSAM + SWAACC@1-100Clients53.9Unverified
4FedASAM + SWAACC@1-100Clients53.86Unverified
5FedAvgACC@1-100Clients49.92Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2Unverified