Distributed fairness constrained optimization framework in federated learning
Optimizing algorithms for global fairness and data privacy in distributed systems.
Innovating Fairness in Federated Learning
We specialize in defining fairness constraints and developing distributed optimization algorithms for federated learning, ensuring global fairness while safeguarding data privacy through rigorous validation and optimization.
Fairness in Federated Learning
We model problems and define constraints to establish fairness in federated learning systems.
Distributed Optimization Algorithms
We develop algorithms ensuring global fairness while protecting users' data privacy during learning.
Validation and Testing
Our framework is validated through simulations and real-world datasets to ensure robust performance.
Optimizing Algorithm Design
We optimize and enhance algorithms based on experimental results and improvement suggestions.
Fairness Algorithms
Developing algorithms for fairness in federated learning environments.
Optimization Techniques
Enhancing distributed optimization for global fairness and privacy.
Validation Process
Testing effectiveness through simulations and real-world datasets.
Improvement Suggestions
Proposing enhancements based on experimental results and evaluations.
Automated Support
API for streamlined processes in federated learning projects.