How Federated Learning Is Helping to Overcome Obstacles in Machine Learning
Federated learning is a machine learning technique that allows multiple parties to collaboratively train a model without sharing their data. This approach is particularly beneficial in scenarios where data sharing is limited by regulations or involves sensitive information, such as in healthcare or finance. By enabling decentralized data sources to contribute to a shared model, federated learning helps overcome challenges related to data privacy, security, and heterogeneity. It also reduces the need for large-scale data centralization, thereby lowering associated storage and computational costs. Industries are increasingly adopting federated learning to enhance AI model performance while maintaining data confidentiality.
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