According to one embodiment, a compression device includes a substring generator and a match information generator. The substring generator receives generates substrings which are stored in a memory. Byte positions of the substrings are different from each other. The match information generator determines a first string, at least part thereof matching at least part of one of the substrings, and outputs match information. The match information includes a position of the memory storing the first string and a length of the at least part of the first string matching the at least part of one of the substrings.

A system and method for a federated deep learning platform utilizing homomorphically-compressed and encrypted data. The system comprises multiple client devices, each with a local dataset, and a central server hosting a deep learning core. Client devices convert local data into codewords, which are also homomorphically encrypted. The central server processes these encrypted codewords without decryption, preserving data privacy. The platform supports at least two architectural variants: a conventional Transformer trained on codewords, and a Latent Transformer operating on latent space vectors. Both variants eliminate the need for embedding and positional encoding layers. The system aggregates encrypted model updates from clients, enabling collaborative learning while maintaining data confidentiality. Additional features comprise differential privacy implementation and adaptive federated optimization techniques. This innovative approach allows for efficient, privacy-preserving distributed learning across diverse datasets, addressing key challenges in federated learning such as data heterogeneity, non-IID distributions, and communication efficiency.

A system and method for deep learning using a large codeword model with hierarchical caching is disclosed. The system processes input prompts into tokens, maps them to codewords using a codebook, and processes these through a machine learning core to generate responses. A sophisticated caching architecture stores and retrieves responses across both local and global cache tiers. The local cache maintains frequently accessed responses on edge devices through short-term and persistent storage components, while the global cache enables knowledge sharing across multiple devices. A context aggregator identifies relationships between cached responses to form comprehensive contextual representations. This hierarchical caching system significantly reduces computational requirements by reusing previously generated responses for similar prompts, while continuously optimizing cache contents based on relevance scoring and usage patterns. The approach enables efficient scaling across distributed environments while maintaining response quality.

Energy aware data compression is disclosed. A storage array may receive data from a client to be compressed. The storage array may request information from a compression awareness engine that is configured to estimate compression times in the context of energy source and energy cost. The storage array makes a decision to compress the data based on the estimates or response received from the compression awareness engine. The data is then compressed and stored in the storage array.