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Enabling Large Language Models to Collaborate and Learn from Each Other While Preserving Privacy

Core Concepts
Large language models can improve their performance by querying more capable remote models, but this poses a significant privacy risk if the local model has access to sensitive data. This work introduces privacy-preserving techniques that allow local models to leverage remote models without revealing private information.
The content discusses the challenge of enabling large language models (LLMs) to collaborate and learn from each other while preserving privacy. LLMs are powerful but come with high inference costs and need to run in data centers far from local contexts where private data is available. Conversely, local models that can run on user devices have more limited capabilities. The authors introduce the first privacy-preserving approach to cascade systems, where a local model (the student) can query a more capable remote model (the teacher) for help, without revealing any private information. They propose three methods for the student to generate queries to the teacher: Describing the problem the student is facing in a high-level way. Generating similar, but novel, unlabeled examples that the teacher can label. Replacing entities in the original examples to mask private information. To evaluate privacy, the authors introduce two metrics: the entity leak metric that counts entities leaked from the original examples, and the mapping leak metric that measures how well a curious teacher with auxiliary information could map the student's queries back to the original examples. Experiments on diverse datasets show that the authors' methods can significantly improve the student's performance compared to baselines, while minimizing privacy leakage. Method 3 (replacing entities) generally achieves the best quality results while leaking few entities, while Method 2 (generating new examples) with grouping offers the strongest privacy metrics.
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"Cascades are a common type of machine learning systems in which a large, remote model can be queried if a local model is not able to accurately label a user's data by itself." "Serving stacks for large language models (LLMs) increasingly use cascades due to their ability to preserve task performance while dramatically reducing inference costs." "Applying cascade systems in situations where the local model has access to sensitive data constitutes a significant privacy risk for users since such data could be forwarded to the remote model."

Deeper Inquiries

How can the privacy-preserving techniques introduced in this work be extended to other modalities beyond text, such as images or audio?

The privacy-preserving techniques introduced in this work, focusing on natural language processing, can be extended to other modalities like images or audio by leveraging similar principles of data minimization and contextual integrity. For images, techniques such as blurring or pixelating sensitive areas can be used to mask private information while still allowing the model to learn from the visual data. In the case of audio, techniques like voice distortion or anonymization can be applied to protect sensitive information. Additionally, encryption methods can be employed to secure the transmission of audio or image data between the local and remote models, ensuring privacy is maintained.

What are the potential limitations or drawbacks of the proposed methods if the remote teacher model is not fully trusted and may attempt to reconstruct private information from the student's queries?

One potential limitation of the proposed methods is the risk of information leakage if the remote teacher model is not fully trusted and attempts to reconstruct private information from the student's queries. Even with privacy-preserving techniques in place, there is always a possibility that a determined adversary could use auxiliary information or sophisticated algorithms to infer sensitive details from the queries. This could lead to privacy breaches and compromise the confidentiality of the data. Additionally, if the teacher model is not transparent about how it processes the queries or if it retains the queries for future use, there is a risk of unauthorized access to private information. Therefore, ensuring the trustworthiness and integrity of the remote teacher model is crucial to prevent privacy violations.

How could the ideas in this work be applied to federated learning scenarios where multiple local models collaborate to solve a task without a central server?

The ideas presented in this work can be applied to federated learning scenarios by adapting the privacy-preserving techniques to the distributed nature of federated learning. In a federated learning setting, where multiple local models collaborate to solve a task without a central server, each local model can implement the privacy-preserving methods to ensure that sensitive information is not exposed during the collaboration process. Techniques such as data minimization, differential privacy, and secure multiparty computation can be utilized to protect the privacy of the data shared between the local models. Additionally, the concept of in-context learning and social learning can be extended to federated learning, allowing the local models to exchange knowledge and insights while preserving the privacy of their individual datasets. By incorporating these ideas, federated learning systems can achieve collaborative task performance improvements while maintaining data privacy and security.