Adaptive Cache Synchronization System for Federated Large Codeword Models

原文（中国語）

A system and method is provided for adaptive sharing of cached results in a distributed machine learning environment. The system receives compressed representations of input data from multiple devices and processes them through a model to generate responses. These responses are stored in both local caches and a shared global cache. Each response is evaluated for reuse and classified for privacy, allowing some to be shared widely, some only with select groups, and others to remain private. Usage patterns from different devices are combined to train models that guide which cached responses should be retained or synchronized. Synchronization is managed adaptively, adjusting when and how information is shared depending on network conditions, utility, and privacy budgets. Before sharing, privacy safeguards such as encryption or differential privacy are applied, and entries are distributed through a coordinating system that ensures consistency and avoids duplication across devices.