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Latest Research Papers in Federated Learning

Latest Research Papers in Federated Learning

Hot Federated Learning Papers

Hot Federated Learning Papers

Related Papers

Applications of Federated Learning:Taxonomy,Challenges,and Research Trends-[2022]
Decentralized Federated Learning: Balancing Communication and Computing Costs-[2022]
Layer-wised Model Aggregation for Personalized Federated Learning-[2022]
A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future Directions-[2021]
A Survey of Incentive Mechanism Design for Federated Learning-[2021]
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection-[2021]
A survey on federated learning-[2021]
A survey on security and privacy of federated learning-[2021]
Achieving security and privacy in federated learning systems: Survey, research challenges and future directions-[2021]
An Efficient Approach for Cross-Silo Federated Learning to Rank-[2021]
An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective-[2021]
Asynchronous Federated Learning on Heterogeneous Devices: A Survey-[2021]
Benchmarking Differential Privacy and Federated Learning for BERT Models-[2021]
Challenges and Future Directions of Secure Federated Learning: A Survey-[2021]
Challenges, Applications and Design Aspects of Federated Learning: A Survey-[2021]
Cost-Effective Federated Learning Design-[2021]
Dispersed Federated Learning: Vision, Taxonomy, and Future Directions-[2021]
Ditto: Fair and robust federated learning through personalization-[2021]
Efficient and Less Centralized Federated Learning-[2021]
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation-[2021]
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning-[2021]
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks-[2021]
Federated Learning Meets Natural Language Processing: A Survey-[2021]
Federated Learning Versus Classical Machine Learning: A Convergence Comparison-[2021]
Federated Learning on Non-IID Data: A Survey-[2021]
Federated Learning: A Distributed Shared Machine Learning Method-[2021]
Federated Learning: Opportunities and Challenges-[2021]
From federated learning to federated neural architecture search: a survey-[2021]
Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated Learning-[2021]
Model-Contrastive Federated Learning-[2021]
Multimodal Federated Learning-[2021]
PFedAtt: Attention-based Personalized Federated Learning on Heterogeneous Clients-[2021]
Vertical Federated Learning without Revealing Intersection Membership-[2021]
Vulnerabilities in Federated Learning-[2021]
A Novel Approach to Machine Learning Application to Protection Privacy Data in Healthcare: Federated Learning-[2020]
A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning-[2020]
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond-[2020]
A review of applications in federated learning-[2020]
Adaptive Personalized Federated Learning-[2020]
Asymmetrical Vertical Federated Learning-[2020]
Collaborative Fairness in Federated Learning-[2020]
Concentrated Differentially Private and Utility Preserving Federated Learning-[2020]
Differentially private cross-silo federated learning-[2020]
Empirical Studies of Institutional Federated Learning For Natural Language Processing-[2020]
FairFed: Cross-Device Fair Federated Learning-[2020]
Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications-[2020]
Federated Learning: Challenges, Methods, and Future Directions-[2020]
How To Backdoor Federated Learning-[2020]
Multi-Center Federated Learning-[2020]
On Safeguarding Privacy and Security in the Framework of Federated Learning-[2020]
Personalized Cross-Silo Federated Learning on Non-IID Data-[2020]
Preserving Data Privacy via Federated Learning: Challenges and Solutions-[2020]
Privacy and Robustness in Federated Learning: Attacks and Defenses-[2020]
Privacy-Aware Federated Learning for Page Recommendation-[2020]
Survey of Personalization Techniques for Federated Learning-[2020]
Survey on Federated Learning Towards Privacy Preserving AI-[2020]
The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems-[2020]
Threats to Federated Learning: A Survey-[2020]
Three Approaches for Personalization with Applications to Federated Learning-[2020]
Throughput-Optimal Topology Design for Cross-Silo Federated Learning-[2020]
Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective-[2020]
Unsupervised Federated Learning for Unbalanced Data-[2020]
VAFL: A Method of Vertical Asynchronous Federated Learning-[2020]
A Hybrid Approach to Privacy-Preserving Federated Learning-[2019]
A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression-[2019]
Active Federated Learning-[2019]
Agnostic Federated Learning-[2019]
Decentralized Federated Learning: A Segmented Gossip Approach-[2019]
Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory-[2019]
Machine Learning for All: A More Robust Federated Learning Framework-[2019]
One-Shot Federated Learning-[2019]
Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator-[2019]
Poisoning Attack in Federated Learning using Generative Adversarial Nets-[2019]
Robust Aggregation for Federated Learning-[2019]
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data-[2019]
Towards Federated Learning at Scale: System Design-[2019]
VerifyNet: Secure and Verifiable Federated Learning-[2019]

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