Multi-objective Evolutionary Federated Learning
时间: 2024-06-04 13:14:04 浏览: 19
Multi-objective evolutionary federated learning (MEFL) is a machine learning approach that combines the principles of multi-objective optimization and federated learning. Multi-objective optimization is a technique that aims to optimize multiple objectives simultaneously, while federated learning is a decentralized machine learning approach that allows multiple devices to train a model collaboratively without sharing their data.
MEFL is designed to overcome the limitations of traditional federated learning approaches, which often suffer from issues related to privacy, communication, and scalability. By using multi-objective optimization, MEFL can optimize the performance of the federated learning algorithm while also addressing these issues.
MEFL works by dividing the optimization problem into multiple objectives, such as minimizing the loss function, reducing communication costs, and preserving privacy. A genetic algorithm is then used to optimize these objectives simultaneously, producing a set of Pareto-optimal solutions that represent the trade-offs between the different objectives.
These Pareto-optimal solutions can then be used to select the best model for deployment, depending on the specific requirements of the application. MEFL has been shown to be effective in a wide range of applications, including image classification, natural language processing, and speech recognition.
Overall, MEFL represents a promising approach to federated learning that can improve the privacy, communication, and scalability of the algorithm while also optimizing its performance.
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