Feature Dictionary Decoding for Low-Bandwidth Distributed ANN Training

The patent introduces an encoding layer as an intermediary between the distributed computing system and the training data. Local encoders on client devices transform raw data into encoded representations before transmission, while a master decoder at the central server reconstructs the data for training purposes. This intermediary encoding mechanism enables distributed training while preserving data privacy, as the encoded data cannot be easily reverse-engineered to reveal original information.

The patent segments the training data processing into separate components: local encoding at client devices and central decoding at the master server. This segmentation allows each component to operate independently with specific functions - local devices perform data encoding and initial processing, while the central server performs model training using decoded data. This division resolves the contradiction by enabling distributed computation while maintaining centralized control over data privacy.

The encoding layer acts as a bandwidth-reducing intermediary by transforming high-dimensional raw data into compressed encoded representations. The encoded data requires significantly less bandwidth for transmission across the distributed network while retaining sufficient information for effective model training. This intermediary transformation resolves the bandwidth constraint in distributed training systems.

The patent changes the parameter representation of training data from raw high-dimensional form to encoded low-dimensional form. By transforming data parameters through encoding functions, the system reduces the quantity of data that needs to be transmitted across the network while maintaining the essential information needed for training complex neural networks with multiple processors.

The encoding/decoding system serves as a faithful intermediary that preserves training accuracy while reducing data transmission volume. The encoder transforms raw data into a compact representation, and the decoder accurately reconstructs the data for training purposes. This intermediary process maintains measurement precision (training accuracy) while dramatically reducing the quantity of data transmitted across the network.

The patent creates encoded copies of the raw training data that can be transmitted efficiently. Instead of transmitting the original high-volume raw data, the system transmits compressed encoded copies that contain sufficient information for accurate training. The master decoder reconstructs the necessary data from these copies, achieving both bandwidth efficiency and training accuracy.