Migrating Models: A Decentralized View on Federated Learning (original) (raw)
Related papers
Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study
arXiv (Cornell University), 2024
Exploiting Features and Logits in Heterogeneous Federated Learning
arXiv (Cornell University), 2022
Communication-efficient Federated Learning through Clustering optimization
HAL (Le Centre pour la Communication Scientifique Directe), 2021
Making a secure framework for Federated Learning
2021
Optimized Federated Learning on Class-Biased Distributed Data Sources
Communications in Computer and Information Science, 2021
Analysis of the Factors Influencing the Predictive Learning Performance Using Federated Learning
Research Square (Research Square), 2023
IEEE Communications Surveys & Tutorials, 2021
Framework for Federated Learning Open Models in e-Government Applications
Interdisciplinary Description of Complex Systems
Edge-Based Communication Optimization for Distributed Federated Learning
IEEE Transactions on Network Science and Engineering, 2022
Advancements in Privacy-Preserving Techniques for Federated Learning: A Machine Learning Perspective
Deleted Journal, 2024
Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
arXiv (Cornell University), 2023
Fast-Convergent Federated Learning With Adaptive Weighting
IEEE Transactions on Cognitive Communications and Networking, 2021
CONTRA: Defending Against Poisoning Attacks in Federated Learning
Computer Security – ESORICS 2021, 2021
FLIP: A New Approach for Easing the Use of Federated Learning
Applied Sciences
Edge-Assisted Democratized Learning Toward Federated Analytics
IEEE Internet of Things Journal, 2022
A Survey on Challenges of Federated Learning
Azerbaijan Journal of High Performance Computing
FedFly: Toward Migration in Edge-Based Distributed Federated Learning
IEEE Communications Magazine
Federated Learning: Opportunities and Challenges
ArXiv, 2021
Federated Learning: Challenges, Methods, and Future Directions
Mr. Murari Kumar Singh (SET Assistant Professor)
IEEE Signal Processing Magazine, 2020
Flex: Flexible Federated Learning Framework
2024
IBM Federated Learning: an Enterprise Framework White Paper V0.1
ArXiv, 2020
FLScalize: Federated Learning Lifecycle Management Platform
IEEE Access
Federated Learning - Methods, Applications and beyond
ESANN 2021 proceedings, 2021
Record and Reward Federated Learning Contributions with Blockchain
2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2019
OpenFL: An open-source framework for Federated Learning
2021
Federated Learning Design and FunctionalModels: Survey
Research Square (Research Square), 2022
Challenges, Applications and Design Aspects of Federated Learning: A Survey
IEEE Access
arXiv (Cornell University), 2023
Federated learning: Applications, challenges and future directions
International Journal of Hybrid Intelligent Systems, 2022
Information Fusion
Applications of Federated Learning; Taxonomy, Challenges, and Research Trends
Electronics, 2022
IEEE Transactions on Network and Service Management, 2023
ModularFed: Leveraging Modularity in Federated Learning Frameworks
arXiv (Cornell University), 2022
Training Mixed-Domain Translation Models via Federated Learning
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence