Microsoft revealed a new side-channel attack called Whisper Leak that enables attackers to infer conversation topics from encrypted traffic between users and language models. The attack works despite HTTPS encryption and can identify sensitive subjects, raising serious privacy concerns. Various AI models have shown vulnerability, prompting some companies to implement countermeasures.

This article discusses the advancements in on-device language models, highlighting their advantages in latency, privacy, cost, and availability. It examines the constraints of mobile devices and explores effective strategies for building smaller, efficient models that can still perform complex tasks.

Apple has unveiled updates to its on-device and server foundation language models, enhancing generative AI capabilities while prioritizing user privacy. The new models, optimized for Apple silicon, support multiple languages and improved efficiency, incorporating advanced architectures and diverse training data, including image-text pairs, to power intelligent features across its platforms.

Privacy-preserving synthetic data can enhance the performance of both small and large language models (LLMs) in mobile applications like Gboard, improving user typing experiences while minimizing privacy risks. By utilizing federated learning and differential privacy, Google researchers have developed methods to synthesize data that mimics user interactions without accessing sensitive information, resulting in significant accuracy improvements and efficient model training. Ongoing advancements aim to further refine these techniques and integrate them into mobile environments.

FlexOlmo introduces a new paradigm for language model training that enables data owners to collaborate without relinquishing control over their data. This approach allows for asynchronous contributions, maintains data privacy, and provides flexible data use, addressing the challenges of traditional AI development. By leveraging a mixture-of-experts architecture, FlexOlmo enhances model performance while minimizing the risk of data extraction.