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 57515800 of 6771 papers

TitleStatusHype
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation0
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges0
Private Wireless Federated Learning with Anonymous Over-the-Air Computation0
PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation0
Privatized Graph Federated Learning0
PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning0
PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems0
Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation0
Probabilistic Federated Learning of Neural Networks Incorporated with Global Posterior Information0
Probabilistic Federated Neural Matching0
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data0
Probabilistic Inference for Learning from Untrusted Sources0
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification0
Production federated keyword spotting via distillation, filtering, and joint federated-centralized training0
ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes0
ProFed: a Benchmark for Proximity-based non-IID Federated Learning0
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning0
PROFL: A Privacy-Preserving Federated Learning Method with Stringent Defense Against Poisoning Attacks0
ProFL: Performative Robust Optimal Federated Learning0
Progressive Size-Adaptive Federated Learning: A Comprehensive Framework for Heterogeneous Multi-Modal Data Systems0
Project Florida: Federated Learning Made Easy0
Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization0
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA0
Prompt-based Personalized Federated Learning for Medical Visual Question Answering0
PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Model0
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications0
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain0
Proof-of-Data: A Consensus Protocol for Collaborative Intelligence0
Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learning0
Proof of Swarm Based Ensemble Learning for Federated Learning Applications0
Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases0
Protea: Client Profiling within Federated Systems using Flower0
Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation0
Protecting Data from all Parties: Combining FHE and DP in Federated Learning0
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning0
Protection Against Reconstruction and Its Applications in Private Federated Learning0
ProtoFL: Unsupervised Federated Learning via Prototypical Distillation0
Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data0
Prototype-Based Layered Federated Cross-Modal Hashing0
Prototype Guided Federated Learning of Visual Feature Representations0
Prototype Helps Federated Learning: Towards Faster Convergence0
Prototype of deployment of Federated Learning with IoT devices0
Provable Federated Adversarial Learning via Min-max Optimization0
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains0
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization0
Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning0
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression0
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation0
Fair Federated Learning via Bounded Group Loss0
Provably Secure Federated Learning against Malicious Clients0
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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