Federated learning: This technique allows AI models to be trained across multiple decentralized devices or servers without exchanging raw data. Federated learning can enhance data privacy by keeping sensitive information local while still benefiting from diverse datasets, but it requires careful implementation to fully realize these privacy benefits.
edge computing: By processing data on local devices rather than in centralized cloud servers, edge computing reduces latency in AI inference and can enhance data locality. This is particularly crucial for applications requiring real-time processing or operating in environments with limited connectivity.
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