Privacy Preseving for AI Assistant | Slides
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FEDERATED LEARNING
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BENEFITS OF FEDERATED LEARNING
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Here's why privacy preserving is important for LLMs: Protecting sensitive data: LLMs have the potential to inadvertently leak sensitive information during training or inference. For example, an LLM trained on news articles might memorize and reproduce identifying details about individuals mentioned in those articles. Privacy-preserving techniques can help prevent such leaks. Building trust with users: People are increasingly concerned about the privacy of their data and are hesitant to interact with technologies that they perceive as being risky. By ensuring that LLMs are privacy-preserving, developers can build trust with users and encourage wider adoption of these models. Complying with regulations: There are a number of laws and regulations that govern the collection, use, and disclosure of personal data. Privacy-preserving LLMs can help organizations comply with these regulations and avoid costly legal penalties.
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